photo:@rewardyfahmi // Unsplash BBM406 Fundamentals of Machine Learning Lecture 3: Kernel Regression, Distance Metrics, Curse of Dimensionality Aykut Erdem // Hacettepe University // Fall 2019
Administrative • Assignment 1 will be out Friday! • It is due November 1 (i.e. in two weeks). • It includes − Pencil-and-paper derivations − Implementing kernel regression − numpy/Python code 2
Movie Recommendation System • MovieLens dataset (100K ratings of 9K movies by 600 users) • You may want to split the training set into train and validation (more on this next week) • The data consists of three tables: − Ratings: userId, movieId, rating, timestamp − Movies: movieId, title, genre − Links: movieId, imdbId, and tmdbId − Tags: userId, movieId, tag, timestamp 3
Recall from last time… Nearest Neighbors • Very simple method • Retain all training data − It can be slow in testing − Finding NN in high dimensions is slow adopted from Fei-Fei Li & Andrej Karpathy & Justin Johnson • Metrics are very important • Good baseline 4
Classification • Input: X - Real valued, vectors over real. - Discrete values (0,1,2,…) - Other structures (e.g., strings, graphs, etc.) • Output: Y - Discrete (0,1,2,...) slide by Aarti Singh and Barnabas Poczos Sports% Anemic%cell% Science% Healthy%cell% News% Y'='Diagnosis' X'='Document' Y'='Topic' X'='Cell'Image' 5
Regression • Input: X - Real valued, vectors over real. - Discrete values (0,1,2,…) - Other structures (e.g., strings, graphs, etc.) • Output: Y slide by Aarti Singh and Barnabas Poczos - Real valued, vectors over real. Stock%Market%% t%% Predic$on% Y'='?' X'='Feb01'' 6
What should I watch tonight? slide by Sanja Fidler 7
What should I watch tonight? slide by Sanja Fidler 8
What should I watch tonight? slide by Sanja Fidler 9
Today • Kernel regression − nonparametric • Distance metrics • Linear regression ( more on Friday ) − parametric − simple model 10
Simple 1-D Regression • Circles are data points (i.e., training examples) that are given to us • The data points are uniform in x , but may be displaced in y t ( x ) = f ( x ) + ε slide by Sanja Fidler with ε some noise • In green is the “true” curve that we don’t know 11
Kernel Regression 12
K-NN for Regression • Given: Training data {( 𝑦 1 , 𝑧 1 ),…, ( 𝑦 n , 𝑧 n )} – Attribute vectors: 𝑦 𝑗 ∈ 𝑌 R – Target attribute 𝑧 𝑗 ∈ • Parameter: – Similarity function: 𝐿 ∶ 𝑌 × 𝑌 → R – Number of nearest neighbors to consider: k • Prediction rule – New example 𝑦′ – K-nearest neighbors: k train examples with largest 𝐿 ( 𝑦 𝑗 , 𝑦′ ) slide by Thorsten Joachims x 0 ) = 1 h ( ~ P x 0 ) y i i 2 knn ( ~ k 13
1-NN for Regression Here, this is the Here, this is the closest closest datapoint datapoint Here, this is the closest Here, this is the datapoint closest datapoint y x slide by Dhruv Batra 14 Figure Credit: Carlos Guestrin
1-NN for Regression • Often bumpy (overfits) slide by Dhruv Batra 15 Figure Credit: Andrew Moore
9-NN for Regression • Often bumpy (overfits) slide by Dhruv Batra 16 Figure Credit: Andrew Moore
Multivariate distance metrics • Suppose the input vectors x 1 , x 2 , … x N are two dimensional: x 1 = ( x 11 , x 12 ) , x 2 = ( x 21 , x 22 ) , … x N = ( x N 1 , x N 2 ) . • One can draw the nearest-neighbor regions in input space. slide by Dhruv Batra Dist( x i , x j ) = ( x i 1 – x j 1 ) 2 + ( x i 2 – x j 2 ) 2 Dist( x i , x j ) = ( x i 1 – x j 1 ) 2 + ( 3x i 2 – 3x j 2 ) 2 The relative scalings in the distance metric affect region shapes 17 Slide Credit: Carlos Guestrin
Example: Choosing a restaurant • In everyday life we need to make • Reviews $ Distance Cuisine • decisions by taking into account (out of 5 (out of 10) stars) lots of factors • • 4 30 21 7 • The question is what weight we 2 15 12 8 put on each of these factors • 5 27 53 9 • (how important are they with individuals’ ¡preferences 3 20 5 6 individuals’ ¡preferences respect to the others). • • ? slide by Richard Zemel 18
Euclidean distance metric Or equivalently, where A slide by Dhruv Batra 19 Slide Credit: Carlos Guestrin
Notable distance metrics (and their level sets) Mahalanobis Scaled Euclidian (L 2 ) slide by Dhruv Batra (non-diagonal A) 20 Slide Credit: Carlos Guestrin
Minkowski distance ! 1 /p n slide by Dhruv Batra X | x i − y i | p D = i =1 Image Credit: By Waldir (Based on File:MinkowskiCircles.svg) 21 [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons
Notable distance metrics (and their level sets) Scaled Euclidian (L 2 ) L 1 norm (absolute) L inf (max) norm slide by Dhruv Batra 22 Slide Credit: Carlos Guestrin
Weighted K-NN for Regression • Given: Training data {( 𝑦 1 , 𝑧 1 ),…, ( 𝑦 n , 𝑧 n )} 𝑦 1 , 𝑧 1 , … , 𝑦 𝑜 , 𝑧 𝑜 • – Attribute vectors: 𝑦 𝑗 ∈ 𝑌 𝑦 𝑗 ∈ 𝑌 – R – Target attribute 𝑧 𝑗 ∈ 𝑧 𝑗 ∈ ℜ – • Parameter: • – Similarity function: 𝐿 ∶ 𝑌 × 𝑌 → R 𝐿 ∶ 𝑌 × 𝑌 → ℜ – – Number of nearest neighbors to consider: k – • • Prediction rule – x’ – New example 𝑦′ ′ 𝐿 𝑦 𝑗 , 𝑦 – – K-nearest neighbors: k train examples with largest 𝐿 ( 𝑦 𝑗 , 𝑦′ ) slide by Thorsten Joachims 23
Kernel Regression/Classification Four things make a memory based learner: • A distance metric − Euclidean (and others) • How many nearby neighbors to look at? − All of them • A weighting function (optional) − w i = exp (-d(x i , query) 2 / σ 2 ) − Nearby points to the query are weighted strongly, far points weakly. The σ parameter is the Kernel Width. Very important. • How to fit with the local points? − Predict the weighted average of the outputs predict = Σ w i y i / Σ w i slide by Dhruv Batra 24 Slide Credit: Carlos Guestrin
Weighting/Kernel functions w i = exp(-d(x i , query) 2 / σ 2 ) slide by Dhruv Batra (Our examples use Gaussian) 25 Slide Credit: Carlos Guestrin
E ff ect of Kernel Width • What happens as σ → inf ? • What happens as σ → 0 ? slide by Dhruv Batra Image Credit: Ben Taskar 26
Problems with Instance- Based Learning • Expensive − No Learning: most real work done during testing − For every test sample, must search through all dataset – very slow! − Must use tricks like approximate nearest neighbour search • Doesn’t work well when large number of irrelevant features • Distances overwhelmed by noisy features slide by Dhruv Batra • Curse of Dimensionality • Distances become meaningless in high dimensions 27
Problems with Instance- Based Learning • Expensive − No Learning: most real work done during testing − For every test sample, must search through all dataset – very slow! − Must use tricks like approximate nearest neighbour search • Doesn’t work well when large number of irrelevant features − Distances overwhelmed by noisy features slide by Dhruv Batra • Curse of Dimensionality − Distances become meaningless in high dimensions 28
Problems with Instance- Based Learning • Expensive − No Learning: most real work done during testing − For every test sample, must search through all dataset – very slow! − Must use tricks like approximate nearest neighbour search • Doesn’t work well when large number of irrelevant features − Distances overwhelmed by noisy features slide by Dhruv Batra • Curse of Dimensionality • Distances become meaningless in high dimensions 29
Curse of Dimensionality • Consider applying a KNN classifier/regressor to data where the inputs are uniformly distributed 1 in the D -dimensional unit cube. • Suppose we estimate the density of class labels around a test point x by “growing” a hyper-cube around x until it contains a desired fraction f of the data points. 0 s • The expected edge length of this cube will be 1 1/ D e D ( f ) = f . 1 0.9 d=10 d=7 0.8 d=5 • If D = 10 , and we want to base our estimate on 0.7 d=3 Edge length of cube 10% of the data, we have e 10 (0.1) = 0.8 , so we 0.6 0.5 need to extend the cube 80% along each 0.4 slide by Kevin Murphy dimension around x . d=1 0.3 0.2 • Even if we only use 1% of the data, we find 0.1 0 e 10 (0.01) = 0.63. — no longer very local 0 0.2 0.4 0.6 0.8 1 Fraction of data in neighborhood 30
Parametric vs Non-parametric Models • Does the capacity (size of hypothesis class) grow with size of training data? –Yes = Non-parametric Models –No = Parametric Models 31
Next Lecture: Linear Regression, Least Squares Optimization, Model complexity, Regularization 32
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