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Predictive Learning from Data LECTURE SET 7 Methods for Regression Electrical and Computer Engineering 1 OUTLINE of Set 7 • Objectives - introduce taxonomy of methods for regression; - describe several representative nonlinear methods; - empirical comparisons illustrating advantages and limitations of these methods • Methods taxonomy • Linear methods • Adaptive dictionary methods • Kernel methods and local risk minimization • Empirical comparisons • Combining methods • Summary and discussion 2 Motivation and issues • Importance of regression for implementation of - classification - density estimation • Estimation of a real-valued function when data (x,y) is generated as y g (x) noise • Major issues for regression - parameterization (representation) of f(x,w) - optimization formulation (~ empirical loss) - complexity control (model selection) • These issues are inter-related 3 Loss function and noise model • Fundamental problem: how to distinguish between true signal and noise? y g (x) noise • Classical statistical view - noise density p(noise) is known statistically optimal loss function in the maximum likelihood sense is L( y, f ( x, w)) logp( y f (x, w)) for Gaussian noise use squared loss (MSE) as empirical loss function 4 Loss functions for linear regression • Consider linear regression only f (x, w) w0 w x • Several unimodal noise models: - Gaussian, Laplacian, unimodal • Statistical view: - Optimal loss for known noise density - asymptotic setting - robust strategies when noise model unknown • Practical situations - noise model unknown - finite (sparse) sample setting 5 (a)Linear loss for Laplacian noise (b)Squared loss for Gaussian noise 3 3 2.5 2.5 2 2 1.5 1.5 1 1 0.5 0.5 0 0 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 6 -insensitive loss (SVM) has common-sense interpretation. Optimal epsilon depends on noise level and sample size 3 2.5 2 1.5 1 0.5 0 -3 -2 -eps 0 eps 2 3 7 Comparison for low-dimensional data: g1 (x) x1 x2 , x [0,1]2 2, n 30 Gaussian noise Laplacian noise 2 2 1 1 0 OLS LM SVM. 0 OLS LM SVM. 8 Comparison for high-dimensional data: g 4 (x) x1 x2 x20 x [0,1]20 1, n 30 Gaussian noise Laplacian noise 10 10 9 9 8 8 7 7 6 6 5 5 4 4 3 3 2 2 1 1 0 OLS LM SVM. 0 OLS LM SVM 9 Methods’ Taxonomy • Recall implementation of SRM: - fix complexity (VC-dimension) - minimize empirical risk (squared-loss) • Two interrelated issues: - parameterization (of possible models) - optimization method • Taxonomy will be based on parameterization: dictionary vs kernel flexibility: non-adaptive vs adaptive 10 m • Dictionary representation f m x, w,V wi gx,vi i0 Two possibilities • Linear (non-adaptive) methods ~ predetermined (fixed) basis functions g i x only parameters wi have to be estimated via standard optimization methods (linear least squares) Examples: linear regression, polynomial regression linear classifiers, quadratic classifiers • Nonlinear (adaptive) methods ~ basis functions gx,vi depend on the training data Possibilities : nonlinear b.f. (in parameters v i ), i.e. MLP feature selection (i.e. wavelet denoising) 11 Example Nonlinear Parameterizations • Basis functions of the form gi (t ) g (xv i bi ) i.e. sigmoid aka logistic function st 1 1 exp t - commonly used in artificial neural networks - combination of sigmoids ~ universal approximator 12 Neural Network Representation • MLP or RBF networks m ˆ wjz j y m j 1 f m x, w,V wi gx,vi i0 W is m 1 z1 z2 zm 1 2 m zj gx,v j V is d m x1 x2 xd - dimensionality reduction - universal approximation property – see example at http://www.mathworks.com/products/demos/nnettlbx/radial/index.html 13 Kernel Methods n • Model estimated as f x Ki x,x i yi i 1 where symmetric kernel function is - non-negative Kx,x' 0 - radially symmetric Kx,x' K x x' - monotonically decreasing with t x x' lim K t 0 t • Duality between dictionary and kernel representation: model ~ weighted combination of basis functions model ~ weighted combination of output values • Selection of kernel functions Ki x,xi non-adaptive ~ depends only on x-values adaptive ~ depends on y-values of training data Note: kernel methods may require local complexity control14 OUTLINE • Objectives • Methods taxonomy • Linear methods Estimation of linear models Equivalent Representations Non-adaptive methods Application Example • Adaptive dictionary methods • Kernel methods and local risk minimization • Empirical comparisons • Combining methods • Summary and discussion 15 Estimation of Linear Models m Dictionary representation f m x, w wi g i x i 0 • Parameters w estimated via least-squares • Denote training data as matrix X x1 ,..., xn and vector of response values y y1 ,...,y n • OLS solution ~ solving matrix equation 1 Z y w Re mp w Zw y 2 n where 1 x1 ... gm x1 g Z ...................... g1 X g 2 X... gm X 1 x n ...gm x n g 16 Estimation of Linear Models (cont’d) • Solution exists if the columns of Z are linearly independent (m < n) Z y w • Solving the normal equation T Z Zw Z y T w Z Z Z y * T 1 T yields OLS solution • Similar math holds for penalized OLS where Rpenw 1 n 2 T Z y w w w w Z Z Z y * T 1 T OLS solution 17 Equivalent Representation m For dictionary representation f m x, w,V wi gx,vi i0 * OLS solution is ˆ Zw Sy y y nXn matrix S ~projection matrix S ZZ Z Z T 1 T y Zw * Column Space of Z ˆ Zw * Sy y Matrix S ~ ‘equivalent kernel’ of an OLS model w* S x, xi gxZ Z g x i T 1 T 18 Equivalent Representation (cont’d) • Equivalent kernel y ˆ Zw* Sy S x, xi gxZ Z g x i may not be local T 1 T • Equivalent ‘kernels’ of a 3-rd degree polynomial 19 Equivalent BFs for Symmetric Kernel • Eigenfunctiondecomposition of a kernel K x, x ei g i x g i x wi g i x i 1 i 1 • The eigenvalues tend to fall off rapidly with I 0.6 0.4 4 BF’s for kernel e2 0.45 e4 0.02 0.2 t2 g t exp 2(0.55) 2 0 e3 0.10 -0.2 e1 1.0 -0.4 -0.6 0 0.2 0.4 0.6 0.8 1 20 Equivalent Representation: summary • Equivalence of representations is due to duality of OLS solution * ˆ Zw Sy y • Equivalent ‘kernels’ are just math artifacts (may be non-local). Notational distinction: K vs S • Practical use of matrix S for: - analytic form of LOO cross-validation - estimating model complexity for penalized linear estimators (ridge regression) 21 Estimating Complexity • Linear estimator is specified via matrix S. Its complexity ~ the number of parameters m of an equivalent linear estimator ˆ var yi E yi E yi E s i y Es i y ˆ ˆ 2 2 E s y Ey E s s s 2 2 T 2 i i i i 2 ave variance of training data var ˆ y trace SST n • Consider an equivalent linear estimator with matrix ˜ S ˜ where S is symmetric of rank m : ˜˜ ˜ ˜ trace SST trace S rank S m 2m so the average variance is var ˆ n y effective DoF of estimator with matrix S is DoF trace SST 22 Non-adaptive methods m • Dictionary representation f m x, w,V wi gx,vi basis functions gx,vi depend only on x-values i0 • Representative methods include: - local polynomials (splines) from statistics where parameters v i are knot locations - RBF networks from neural networks where parameters v i are RBF center and width Only non-adaptive implementation of RBF will be considered 23 Local polynomials and splines • Problem setting: data interpolation(univariate) problem with polynomials local low-order polynomials knot location strategies: subset of training samples, or uniformly spaced in x-domain. 24 RBF Networks for Regression • RBF networks m ˆ wjz j y m x v j 1 f m x, w w j g j w0 j 1 j W is m 1 typically local BFs z1 z2 zm 1 2 m zj gx,v j • Training ~ estimating -parameters of BF’s V is d m -linear weights W x1 x2 xd - non-adaptive implementation (TBD) - adaptive implementation 25 Non-adaptive RBF training algorithm 1. Choose the number of basis functions (centers) m. 2. Estimate centers v j using x-values of training data via unsupervised training (SOM, GLA, clustering etc.) 3. Determine width parameters j using heuristic: For a given center v j (a) find the distance to the closest center: r j min vk v j for all k j k (b) set the width parameter j rj where parameter controls degree of overlap between adjacent basis functions. Typically 1 3 4. Estimate weights w via linear least squares (minimization of the empirical risk). 26 Application Example: Predicting NAV of Domestic Mutual Funds • Motivation • Background on mutual funds • Problem specification + experimental setup • Modeling results • Discussion 27 Background: pricing mutual funds • Mutual funds trivia • Mutual fund pricing: - priced once a day (after market close) NAV unknown when order is placed • How to estimate NAV accurately? Approach 1: Estimate holdings of a fund (~200- 400 stocks), then find NAV Approach 2: Estimate NAV via correlations btwn NAV and major market indices (learning) 28 Problem specs and experimental setup • Domestic fund: Fidelity OTC (FOCPX) • Possible Inputs: SP500, DJIA, NASDAQ, ENERGY SPDR • Data Encoding: Output ~ % daily price change in NAV Inputs ~ % daily price changes of market indices • Modeling period: 2003. • Issues: modeling method? Selection of input variables? Experimental setup? 29 Experimental Design and Modeling Setup Possible variable selection: Mutual Funds Input Variables Y X1 X2 X3 FOCPX ^IXIC - - FOCPX ^GSPC ^IXIC - FOCPX ^GSPC ^IXIC XLE • All variables represent % daily price changes. • Modeling method: linear regression • Data obtained from Yahoo Finance. • Time period for modeling 2003. 30 Specification of Training and Test Data Year 2003 1, 2 3, 4 5, 6 7, 8 9, 10 11, 12 Training Test Training Test Training Test Training Test Training Test Two-Month Training/ Test Set-up Total 6 regression models for 2003 31 Results for Fidelity OTC Fund (GSPC+IXIC) Coefficients w0 w1 (^GSPC) W2(^IXIC) Average -0.027 0.173 0.771 Standard Deviation (SD) 0.043 0.150 0.165 Average model: Y =-0.027+0.173^GSPC+0.771^IXIC ^IXIC is the main factor affecting FOCPX’s daily price change Prediction error: MSE (GSPC+IXIC) = 5.95% 32 Results for Fidelity OTC Fund (GSPC+IXIC) 140 130 120 Daily Account Value 110 100 90 FOCPX Model(GSPC+IXIC) 80 1-Jan-03 20-Feb-03 11-Apr-03 31-May-03 20-Jul-03 8-Sep-03 28-Oct-03 17-Dec-03 Date Daily closing prices for 2003: NAV vs synthetic model 33 Results for Fidelity OTC Fund (GSPC+IXIC+XLE) Coefficients w0 w1 (^GSPC) W2(^IXIC) W3(XLE) Average -0.029 0.147 0.784 0.029 Standard Deviation (SD) 0.044 0.215 0.191 0.061 Average Model: Y=-0.029+0.147^GSPC+0.784^IXIC+0.029XLE ^IXIC is the main factor affecting FOCPX daily price change Prediction error: MSE (GSPC+IXIC+XLE) = 6.14% 34 Results for Fidelity OTC Fund (GSPC+IXIC+XLE) 140 130 120 Daily Account Value 110 100 90 FOCPX Model(GSPC+IXIC+XLE 80 ) 1-Jan-03 20-Feb-03 11-Apr-03 31-May-03 20-Jul-03 8-Sep-03 28-Oct-03 17-Dec-03 Date Daily closing prices for 2003: NAV vs synthetic model 35 Effect of Variable Selection Different linear regression models for FOCPX: • Y =-0.035+0.897^IXIC • Y =-0.027+0.173^GSPC+0.771^IXIC • Y=-0.029+0.147^GSPC+0.784^IXIC+0.029XLE • Y=-0.026+0.226^GSPC+0.764^IXIC+0.032XLE-0.06^DJI Have different prediction error (MSE): • MSE (IXIC) = 6.44% • MSE (GSPC + IXIC) = 5.95% • MSE (GSPC + IXIC + XLE) = 6.14% • MSE (GSPC + IXIC + XLE + DJIA) = 6.43% (1) Variable Selection is a form of complexity control (2) Good selection can be performed by domain experts 36 Discussion • Many funds simply mimic major indices statistical NAV models can be used for ranking/evaluating mutual funds • Statistical models can be used for - hedging risk and - to overcome restrictions on trading (market timing) of domestic funds • Since 70% of the funds under-perform their benchmark indices, better use index funds 37 OUTLINE • Objectives • Methods taxonomy • Linear methods • Adaptive dictionary methods - additive modeling and projection pursuit - MLP networks - CART and MARS • Kernel methods and local risk minimization • Empirical comparisons • Combining methods • Summary and discussion 38 Additive Modeling & Projection Pursuit • Additive models have parameterization for m regression f x,V g x,v w j j 0 j 1 where g j x,v j is an adaptive basis function • Backfitting is a greedy optimization approach for estimating basis functions sequentially: - basis function g k x, v k is estimated by holding all other basis functions j k fixed 39 • By fixing all basis functions j k the empirical risk (MSE) can be decomposed as 1 n Remp V yi f x i , V 2 n i 1 2 1 yi g j x i , v j w0 g k x i , v k n n i 1 j k 1 n ri g k x i , v k 2 n i 1 Each basis function g k x, v k is estimated via an iterative backfitting algorithm (until some stopping criterion is met) Note: ri can be interpreted as the response variable for the adaptive method g k x, v k 40 Backfitting Algorithm • Consider regression estimation of a function of two variables of the form y g1 x1 g 2 x2 noise from training data ( x1i , x2i , yi ) i 1,2,...,n For example t ( x1 , x2 ) x1 sin(2x2 ) x 0,1 2 2 Backfitting method: (1) estimate g1 x1 for fixed g 2 (2) estimate g 2 x2 for fixed g 1 iterate above two steps • Estimation via minimization of empirical risk n Remp g1 ( x1 ), g 2 ( x2 ) yi g1 ( x1i ) g 2 ( x2i ) 2 1 n i 1 1 n ( first _ step ) yi g 2 ( x2i ) g1 ( x1i ) 2 n i 1 1 n ri g1 ( x1i ) 2 41 n i 1 Backfitting Algorithm(cont’d) • Estimation of g1 ( x1 ) via minimization of MSE 1 n Remp g1 ( x1 ) ri g1 ( x1i ) min 2 n i 1 • This is a univariate regression problem of estimating g1 x1 from n data points ( x1i , ri ) where ri yi g 2 ( x2i ) • Can be estimated by smoothing (kNN regression) • Estimation of g 2 x2 (second_step) proceeds in a similar manner, via minimization of 1 n Remp g 2 ( x2 ) ri g 2 ( x2i ) where ri yi g1 ( x1i ) 2 n i 1 42 Projection Pursuit regression • Projection Pursuit is an additive model: m f x,V, W g x,v w w j j j 0 where basis functions g j z,v j are univariate j 1 functions (of projections) • Backfitting algorithm is used to estimate iteratively (a) basis functions (parameters v j) via scatterplot smoothing (b) projection parameters w j (via gradient descent) 43 EXAMPLE: estimation of a two-dimensional fct via projection pursuit (a) Projections are found that minimize unexplained variance. Smoothing is performed to create adaptive basis functions. (b) The final model is a sum of two univariate adaptive basis functions. 44 Multilayer Perceptrons (MLP) • Recall MLP networks m ˆ wjz j y j 1 for regression W is m 1 where d v x v sx v z1 z2 zm gx,vi s i 0 zj gx,v j k ik i k 1 1 2 m 1 st 1 exp t V is d m or expt expt st tanht expt expt x1 x2 xd • Parameters (weights) estimated via backpropagation 45 Details of backpropagation 1 • Sigmoid activation st - picture? 1 exp t • simple derivative st st 1 st Poor behaviour for large t ~ saturation • How to avoid saturation? - Proper initialization (small weights) - Pre-scaling of inputs (zero mean, unit variance) • Learning rate schedule (initial, final) • Stopping rules, number of epochs • Number of hidden units 46 Additional enhancements • The problem: convergence may be very slow for error functional with different curvatures: • Solution: add momentum term to smooth oscillations wk 1 wk kzk wk where wk wk wk 1 and is momentum parameter 47 Various forms of complexity control • MLP topology ~ number of hidden units • Constraints on parameters (weights) ~ weight decay • Type of optimization algorithm (many versions of backprop., other opt. methods) • Stopping rules • Initial conditions (initial ‘small’ weights) • So many factors make it difficult to control complexity; usually vary 1 complexity factor while keeping all others fixed 48 Toy example: regression • Data set: 25 samples generated using sine-squared target function with Gaussian noise (st. deviation 0.1). • MLP network (two hidden units) underfitting 49 Toy example: regression • Data set: 25 samples generated using sine-squared target function with Gaussian noise (st. deviation 0.1). • MLP network (10 hidden units) near-optimal 50 Backpropagation for classification m • Original MLP is for regression ˆ wjz j y j 1 (as introduced above) W is m 1 z1 z2 zm 1 2 m zj gx,v j V is d m • For classification: x x x 1 2 d - use sigmoid output unit - during training, use real-values 0/1 for class labels - during operation, threshold the output of a trained MLP classifier at 0.5 to predict class labels (as in HW2) 51 Toy example: classification • Data set: 250 samples ~ mixture of gaussians, where Class 0 data has centers (-0.3, 0.7) and (0.4, 0.7), and Class 1 data has centers (-0.7, 0.3) and (0.3, 0.3). The variance of all gaussians is 0.03. • MLP classifier (two hidden units) 52 Toy example: classification • MLP classifier (six hidden units) ~ near optimal solution 53 MLP architectures • Supervised learning: single output i.e., classification, regression • Supervised learning: 1 2 k W is m k multiple outputs sx v j 1 2 m V v1 v 2 ...vm V is d m x1 x2 xd m f x,w ,V wj sx v j w0 for each output unit j 1 In matrix notation: Fx,W,V sxVW • Unsupervised learning (data compression) 54 NetTalk (Sejnowski and Rosenberg, 1987) One of the first successful applications of backpropagation: http://www.cnl.salk.edu/ParallelNetsPronounce/index.php • Goal: Learning to read (English text) aloud, i.e. Learn Mapping: English text phonemes using MLP network • Network inputs encode 7-letter window (the 4-th letter in the middle needs to be pronounced) • Network outputs encode phonemes (used in English) • The MLP network is trained using labeled data (both individual words and unrestricted text) 55 NetTalk architecture Input encoding: 7x29 = 203 units Output encoding: 26 units (phonemes) Hidden layer: 80 hidden units 56 MLP networks: summary • MLP and Projection Pursuit models have the same mathematical parameterization but very different statistical properties: MLP model ~ sum of many basis functions of projections (basis functions are the same) PP model ~ sum of a few basis functions of projections (basis functions are adapted to data) • Model complexity control for MLP: - may be tricky as it depends on many factors (optimization method, weight initialization, network topology) - in practice, tune just one factor (with others fixed) using resampling NOTE: implementation of resampling may be tricky (with nonlinear optimization) 57 Regression Trees (CART) • Minimization of empirical risk (squared error) via partitioning of the input space into regions m f x w j I R j x j 1 • Example of CART partitioning for a function of 2 inputs split 1 x1 ,s1 x2 1 s4 R1 2 x2 ,s2 R4 R5 R1 s2 3 x2 ,s3 4 x1 ,s4 R3 s3 R2 x1 R2 R3 R4 R5 s1 58 Growing CART tree • Recursive partitioning for estimating regions (via binary splitting) • Initial Model ~ Region R 0 (the whole input domain) is divided into two regions R 1 and R 2 • A split is defined by one of the inputs(k) and split point s • Optimal values of (k, s) chosen so that splitting a region into two daughter regions minimizes empirical risk • Issues: - efficient implementation (selection of opt. split) - optimal tree size ~ model selection(complexity control) • Advantages and limitations 59 CART model selection • Model selection strategy (1) Grow a large tree (subject to min leaf node size) (2) Tree pruning by selectively merging tree nodes • The final model ~ minimizes penalized risk R pen , Remp T where empirical risk ~ MSE number of leaf nodes ~ T regularization parameter ~ • Note: larger smaller trees • In practice: often user-defined (splitmin in Matlab) 60 Example: Boston Housing data set • Objective: to predict the value of homes in Boston • Data set ~ 506 samples total Output: value of owner-occupied homes (in $1,000’s) Inputs: 13 variables 1. CRIM per capita crime rate by town 2. ZN proportion of residential land zoned for lots over 25,000 sq.ft. 3. INDUS proportion of non-retail business acres per town 4. CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) 5. NOX nitric oxides concentration (parts per 10 million) 6. RM average number of rooms per dwelling 7. AGE proportion of owner-occupied units built prior to 1940 8. DIS weighted distances to five Boston employment centres 9. RAD index of accessibility to radial highways 10. TAX full-value property-tax rate per $10,000 11. PTRATIO pupil-teacher ratio by town 12. B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 13. LSTAT % lower status of the population 61 Example CART trees for Boston Housing 1.Training set: 450 samples Splitmin =100 (user-defined) R0 R1 R2 62 Example CART trees for Boston Housing 2.Training set: 450 samples Splitmin =50 (user-defined) R0 R1 R2 63 Example CART trees for Boston Housing 3.Training set: 455 samples Splitmin =100 (user-defined) Note: CART model is sensitive to training samples (vs model 1) 64 Decision Trees: summary • Advantages - speed - interpretability - different types of input variables • Limitations: sensitivity to - correlated inputs - affine transformations (of input variables) - general instability of trees • Variations: ID3 (in machine learning), linear CART 65 MARS • MARS features and improvements (over CART) - continuous approximation (via tensor-product splines) - greedy selection of low-order basis functions - variable selection (local + global) • MARS complexity control - lack of fit measure based on Generalized Cross Validation (GCV), i.e. MSE on the training set penalized by model complexity (tree size) • MARS applicability - good for high- and low-D problems with a small number of low-order interactions (local or global) Interaction occurs when the effect of one variable (on the output) depends on the level of other variable 66 MARS Basis Functions Truncated Splines (+) (-) t x t x a pair of truncated linear splines b x t y Basic building block x t 67 Tensor-Product Splines Multivariate Splines - Tensor product gx,u,v j x j v j d q u j 1 adaptive selection of knot locations - Valid knot locations 68 MARS Tree Structure B1 x 1 B2 x B1 x B3 x B1 x B4 x B1 x B5 x B1 x b x1 t1 b x1 t1 b x 2 t 2 b x 2 t2 B6 x B3 x B7 x B3 x b x 3 t3 b x 3 t 3 7 • Each node ~ active basis function y ai Bi x i1 • Basis functions estimated recursively • On each path, variables are split at most once • Depth of tree indicates interaction level 69 Algorithm for MARS • Forward stepwise: search over each node to find - split variable - split point t - coefficients (weights of basis functions) that minimize lack of fit criterion (GCV) • Backwards stepwise: remove nodes which cause - decrease of gcv or - the smallest increase of gcv hl R r r p 1 p 2 • R GCV criterion n e mp where h mars m 1 70 MARS Summary • Advantages - Provides variable subset selection - Continuous approximation - Works well for low-order interactions and additive functions - Interpretable • Limitations - sensitive to coordinate rotation - problems in dealing with collinear variables - stability of MARS modeling, for small samples 71 OUTLINE • Objectives • Methods taxonomy • Linear methods • Adaptive dictionary methods • Kernel methods and local risk minimization - kernel methods and local risk minimization - Generalized Memory-Based Learning - Constrained Topological Mapping • Empirical comparisons • Combining methods • Summary and discussion 72 Local Risk Minimization • Local learning (memory-based learning): estimate a function at a single point x0 • Local risk minimization K x, x 0 R , ;x 0 Ly, f x, px,y dxdy x 0 local neighborhood function K x,x 0 normalizing function x 0 K x, x0 pxdx • The goal is minimization of local prediction risk over a set of f x, and over the kernel width using only training data 73 Practical Implementation of LRM • Sumultaneous minimization of local risk over f x, and over kernel width is hard • Practical methods assume fixed (constant or linear) parameterization and then adjust only the kernel width. Local Estimation at a point x0 : (1) Select approximating functions of fixed low complexity, and select kernel function (i.e. gaussian or hard threshold). (2) Select optimal kernel width, providing min estimated local risk. That is, selectively decrease training sample (near x0 ) to make an estimate. 74 LRM and Kernel Methods • Consider minimization of Local Empirical Risk 1 n Re mp local K x i , x0 yi f x i , 2 n i 1 assuming constant parameterization f x,w0 w0 local average f x w 1 n 0 0 yi K x i ,x 0 n i 1 (similarly, for local linear parameterization) • Solution to LRM leads to adaptive kernel method, because the kernel width is adapted to data at each estimation point x0. However, adaptive selection of kernel width is hard. 75 Practical Selection of Kernel Width • Global Adaptive Approach: the kernel width is estimated globally, independent of a particular estimation point. • Global model selection for k-nn regression: For a given value of k: (1) Compute a local estimate ˆ i at each input x i y (2) Compute total empirical risk of these estimates 1 n Re mp k yi ˆ i 2 y n i 1 (3) Estimate prediction risk using (analytic) model selection criterion. Minimize this risk through appropriate selection of k. 76 Generalized Memory-Based Learning • For a given new input, an output is estimated via local learning using past data • GMBL implements locally weighted linear approximation minimizing n 1 Re mp localw,w0 K xi ,x 0 w x i w0 yi 2 n i 1 q d x x 2 v 2 where the kernel Kx, x,v k k k k 1 has adaptable width and scale parameters estimated via cross-validation using all data 77 Constrained Topological Mapping Recall applying SOM to regression problem Nonadaptive CTM Approach: Given training data (x,y) perform 1. Dimensionality reduction xz (Apply SOM to x-values of training data) 2. Apply kernel regression to estimate y=f(z) at discrete points in z-space 78 Adaptive CTM Implementation • Batch implementation • Local linear modeling for each CTM unit n 2 1 Re mp localw j ,w0 j Kzi , j w j x i w0 j yi n i 1 • Variable selection via adaptive scaling b d c j x i v v l jl xil vl w jl 2 2 2 c where l 1 j 1 • Final neighborhood width (model complexity) selected via cross-validation 79 OUTLINE • Objectives • Methods taxonomy • Linear methods • Adaptive dictionary methods • Kernel methods and local risk minimization • Empirical comparisons • Combining methods • Summary and discussion 80 Empirical Comparisons Ref: Cherkassky et al. (1996), Comparison of adaptive methods for function estimation from samples, IEEE Transactions on Neural Networks, 7, 969-984 • Challenge of comparisons - who performs comparisons (experts vs general users) - goals of comparison - synthetic vs real-life data - importance of experimental procedure (i.e. for model selection, double resampling etc.) 81 Example comparison study Time series prediction (Weigend & Gershenfeld 1992) • Performed by experts on time series 1K-100K samples long • Lessons learned/ conclusions - knowledge of application domain is important (simplistic black-box approaches usually fail) - successful methods are nonlinear - custom/manual control of method’s parameters (model selection) 82 Application of Adaptive Methods 1. Choose flexible method (parameterization) 2. Choose complexity parameter - automatic (from data) or user-selected 3. Estimate model (from training data) 4. Estimate prediction performance(on test data) NOTE: empirical comparison (of methods) is difficult because prediction performance depends on all factors (1) - (3), in addition to data itself 83 Example Comparison Study • Objectives and assumptions - non-expert users - public-domain s/w for regression methods - manual model selection using test set; just 1 or 2 user-defined parameters - off-line training (batch mode) - comparison focus on methods’ parameterization (1) and model selection (2) for different synthetic data sets 84 Objectives and assumptions (cont’d) • Methods in XTAL package k- nearest neighbors regression (k-NN) Linear Regression (LR) Projection Pursuit (PPR) Multivariate Adaptive Regression Splines (MARS) Generalized Memory-Based Learning (GMBL) Constrained Topological Mapping (CTM) Artificial Neural Network / backpropagation (ANN) • Synthetic data low- and high-dimensional uniformly distributed in x-space 85 Experimental Set-Up • Specification of properties of synthetic data: target functions, training/test set size, x-distribution, noise level performance metric: NRMS error (for test set) 4 parameter settings for each method: KNN: k = 2, 4, 8, 16 GMBL: no parameters (run only once) CTM: smoothing parameter = 0, 2, 5, 9 MARS: smoothing parameter = 0, 2, 5, 9 PPR: number of terms (in the smallest model) = 1, 2, 5, 8 ANN: number of hidden units = 5, 10, 20, 40 86 • Training Data - uniform distribution (random and spiral) - size: smalle (25), medium (100), large (400) - noise level: none, medium(SNR=4), large (SNR=2) • Test data: 961 samples (no noise) - spaced uniformly on 2D grid (for 2-dimensional data) - randomly sampled for high-dimensional data 87 • 2D Target functions Function 1 Function 2 Function 3 Function 4 88 • 2D Target functions (cont’d) Function 5 Function 6 Function 7 Function 8 89 Comparison Summary BEST WORST Prediction accuracy (dense samples) ANN KNN, GMBL Prediction accuracy (sparse samples) GMBL, KNN MARS, PP Additive target functions MARS, PP KNN, GMBL Harmonic functions CTM, ANN PP Radial functions ANN, PP KNN Robustness wrt parameter tuning ANN, GMBL PP Robustness wrt sample properties ANN, GMBL PP, MARS • Methods performance - similar at dense (large) samples - uneven at sparse samples and depends significantly on the properties of data 90 Comments on Specific Methods • Comparison metrics: (a) generalization (b) robust parameter tuning (c)robust to data characteristics • kNN and GMBL: (a) inferior to other methods when accurate prediction is possible (b) very robust (c) very robust 91 Comments on Specific Methods • MARS (a) good for additive functions (b) somewhat brittle (c) rather unpredictable • PPR (a) good for additive functions, functions of linear combinations of inputs; poor for harmonic functions (b) brittle (c) rather unpredictable 92 Comments on Specific Methods • ANN (a) good for functions of linear combinations, harmonic and radial-type functions (b) very robust (c) very predictable • CTM (a) very good for harmonic functions, poor functions of linear combinations (b) robust (c)predictable; best for spiral distribution in x-space 93 Conclusions and Caveats • Comparison results always biased by - selection of data sets - s/w implementation of adaptive methods - (expert) user bias • Relative performance varies with properties of data sets (i.e. sample size, noise level etc) • Heuristic optimization methods (ANN, CTM) are computationally intensive but often more robust than faster statistical methods • Nonlinear methods should be robust: only for robust methods it is possible to develop automatic parameter tuning (complexity control) 94 OUTLINE • Objectives • Methods taxonomy • Linear methods • Adaptive dictionary methods • Kernel methods and local risk minimization • Empirical comparisons • Combining methods • Summary and discussion 95 Motivation for Combining Methods • General setting (used in this course) - given training data set - apply different learning methods - select the best model (method) Learning Method + Data Predictive Model • Why discard other models? Motivation (cont’d) Learning Method + Data Predictive Model • Theoretical and empirical evidence - no single ‘best’ method exists • Always possible to find: - best method for given data set - best data set for given method • Philosophical + statistical connections, Eastern philosophy, Bayesian averaging: Combine several theories (models) explaining the data Strategies for Combining Methods • Predictive model depends on 3 factors (a) parameterization of admissible models (b) random training sample (c) empirical loss (for risk minimization) • Three combining strategies (for improved generalization) 1. Different (a), the same (b) and (c) Committee of Networks, Stacking, Bayesian averaging 2. Different (b), the same (a) and (c) Bagging 3. Different (c), the same (a) and (b) Boosting Combining Strategy 1 • Apply N different methods (parameterizations) to the same data N distinct models • Form (linear) combination of N models Combining Strategy 1 (cont’d) Design issues: • What parameterizations (methods) to use? - as different as possible • How many component models? • How to combine component models? - via empirical risk minimization (neural network strategy) - Bayesian averaging (statistical strategy) Committee of networks approach Given training data x i ,yi i 1,...,n • Estimate N candidate (regression) models f 1 x, 1 , f 2 x, 2 ,..., f N x, N * * * using different methods • Construct the combined model as 1 N N j 1 f com x, j f j x, * j where coefficients j are estimated via min. of emp. risk N j 1 n R f com x i , y i 1 2 under n i 1 constraints j 1 j 0 Example of Committee Approach y 0.8sin2 x 0.2x 2 • Regression data set: with x-values uniform in [0,1] and noise variance 2 0.25 • Regression methods used m1 1 (a) polynomial f polyx ,um uj x j m 1 1 (b) trigonometric f trigx ,v m , wm vj sinjx w j cos jx w0 j 0 2 2 2 j 1 (c) combined (Committee of Networks) f comb1x , f poly x,um 1 f trigx,v m ,wm 1 2 2 • Model selection: - VC model selection for (a) and (b) - empirical risk minimization for (c) Comparison: 25 training samples Red ~ target function; Blue dashed ~ polynomial model Blue dotted ~ trigonometric model; Black ~ combined model Comparison: 50 training samples Red ~ target function; Blue dashed ~ polynomial model Blue dotted ~ trigonometric model; Black ~ combined model Comparison results 25 training samples: Model f(x) MSE(f(x),target) Poly (d=3) 0.0857 Trigon (d=3) 0.0237 Combined 0.0239 (alpha=0.5) 50 training samples: Model f(x) MSE(f(x),target) Poly (d=4) 0.0046 Trigon (d=4) 0.0044 Combined 0.0038 (alpha=0.2) Stacking approach Given training data x i ,yi i 1,...,n • Estimate N candidate (regression) models f 1 x, 1 , f 2 x, 2 ,..., f N x, N * * * using different methods • Construct the combined model as 1 N N j 1 f com x, j f j x, * j where coefficients j are estimated via resampling N j 1 n R f com x i , y i 1 2 under n i 1 constraints j 1 j 0 Empirical Comparison • The same data set and experimental setup • Committee approach ~ Comb 1 Stacking approach ~ Comb 2 107 Summary and Discussion • Linear (nonadaptive) methods for regression - theoretically well-understood - effective methods for complexity control • Nonlinear (adaptive) methods - inherently complex (non-tractable optimization) - difficult to apply analytic model selection and resampling - no single best method exists for all data sets • Combining methods often result in better predictions 108

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