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0 Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher

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1 Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1) Introduction Maximum-Likelihood Estimation Example of a Specific Case Gaussian Case: unknown and Bias Appendix: ML Problem Statement

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2 Data availability in a Bayesian framework To design optimal classifier, need: P( i ) (priors) P(x | i ) (class-conditional densities) Unfortunately, rarely have this complete information! Design a classifier from a training sample Easy to estimate prior Samples are often too small to estimate class-conditional (large dimension of feature space!) 1 Introduction

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3 Normality of P(x | i ) P(x | i ) ~ N( i, i ) Characterized by 2 parameters Estimation techniques Maximum-Likelihood (ML) and the Bayesian estimations Results are nearly identical, but the approaches are different 1 A priori information about the problem

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4 Ml Estimation Parameters are fixed but unknown! Obtain best parameters by maximizing probability of obtaining the samples observed -- argmax theta { P( D | theta ) } Bayesian methods view parameters as random variables having some known distribution compute POSTERIOR distribution In either approach, classification rule == P( i | x) 1 ML vs Bayesian Methods

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5 Good convergence properties as the sample size increases Simpler than any other alternative techniques General principle Assume we have c classes and P(x | j ) ~ N( j, j ) P(x | j ) P (x | j, j ) where: 2 Maximum-Likelihood Estimation

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6 Use training samples to estimate = ( 1, 2, …, c ), i is associated with category i (i = 1, 2, …, c) Suppose that D contains n samples, {x 1, x 2,…, x n } ML estimate of is, by definition, the value that maximizes P(D | ) “It is the value of that best agrees with the actually observed training sample” 2 Details of ML Estimation

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7 2

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8 = ( 1, 2, …, p ) t = gradient operator l( ) = ln P(D | ) is log-likelihood function New problem statement: determine that maximizes log-likelihood 2 Optimal Estimation

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9 l = 0 Not sufficient (local opt, …) Check 2 nd derivative 2 Necessary conditions for Optimum

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10 P(x i | ) ~ N( , ) (Samples drawn from multivariate normal population) = ML estimate for must satisfy: 2 Specific case: unknown

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11 Multiply by , rearranging… Just arithmetic average of training sampls! Conclusion: If P(x k | j ) (j = 1, 2, …, c) is d-dimensional Gaussian; then estimate = ( 1, 2, …, c ) t to perform optimal classification! 2 Specific case: unknown (con’t)

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12 Gaussian Case: unknown and = ( 1, 2 ) = ( , 2 ) 2 ML Estimation (unknown and )

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13 Combine (1) and (2) to obtain: 2 Results …

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14 ML estimate for 2 is biased An elementary unbiased estimator for is: 2 Bias

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15 Let D = {x 1, x 2, …, x n } P(x 1,…, x n | ) = k=1 n P(x k | ) |D| = n Goal: determine (value of that makes this sample the most representative) 2 ML Problem Statement

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16 |D| = n x1x1 x2x2 xnxn.................. x 11 x 20 x 10 x8x8 x9x9 x1x1 N( j, j ) = P(x j | 1 ) D1D1 DcDc DkDk P(x j | n ) P(x j | k ) 2

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17 = ( 1, 2, …, c ) Find such that: 2 Problem Statement

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18 Bayesian Decision Theory Chapter 2 (Sections 2.3-2.5) Minimum-Error-Rate Classification Classifiers, Discriminant Functions, Decision Surfaces The Normal Density

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19 Minimum-Error-Rate Classification Actions are decisions on classes If take action i and the true state of nature is j then: decision is correct iff i = j (else in error) Seek decision rule that minimizes the probability of error (aka error rate )

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20 Conditional risk: “The risk corresponding to this loss function is the average probability error” Zero-one loss function

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21 As R( i | x) = 1 – P( i | x) … to minimize risk, maximize P( i | x) For Minimum error rate Decide i if P ( i | x) > P( j | x) j i Minimum Error Rate

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22 As 0/1 loss, decide 1 if Decision Boundary If is the zero-one loss function which means:

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24 Classifiers, Discriminant Functions and Decision Surfaces The multi-category case Set of discriminant functions g i (x), i = 1,…, c Classifier assigns feature vector x to class i if: g i (x) > g j (x) j i

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26 Let g i (x) = - R( i | x) (max discriminant corresponds to min risk!) For minimum error rate, use g i (x) = P( i | x) (max discrimination corresponds to max posterior!) g i (x) P(x | i ) P( i ) Useg i (x) = ln P(x | i ) + ln P( i ) (ln: natural logarithm) Max Discriminant

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27 Dividee feature space into c decision regions if g i (x) > g j (x) j i then x is in R i ( R i assign x to i ) Two-category case Classifier is “dichotomizer” iff it has two discriminant functions g 1 and g 2 Let g(x) g 1 (x) – g 2 (x) Decide 1 if g(x) > 0 ; Otherwise decide 2 Decision Regions

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28 Computing g(x)

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30 Univariate Normal Density Continuous density, analytically tractable Many processes are asymptotically Gaussian Handwritten characters Speech sounds ideal or prototype corrupted by random process (central limit theorem) where: = mean (or expected value) of x 2 = expected squared deviation or variance

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32 Multivariate normal density in d dimensions is: where: x = (x 1, x 2, …, x d ) t (t stands for the transpose vector form) = ( 1, 2, …, d ) t mean vector = d*d covariance matrix | | and -1 are determinant and inverse respectively Multivariate Normal Density

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33 Bayesian Decision Theory III Chapter 2 (Sections 2-6,2-9) Discriminant Functions for the Normal Density Bayes Decision Theory – Discrete Features

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34 Discriminant Functions for the Normal Density Recall… minimum error-rate classification achieved by discriminant function g i (x) = ln P(x | i ) + ln P( i ) Multivariate normal

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35 Special Case… Independent variables; Constant Variance i = 2 I ( I identity matrix) where … Linear Discriminant Function i is “threshold for i th category

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36 Classifier using linear discriminant function is called “a linear machine” Decision surfaces for a linear machine are pieces of hyperplanes defined by: g i (x) = g j (x) Linear Machine

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38 The hyperplane separating R i and R j always orthogonal to the line linking the means! Classification Region HERE!!!

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41 Case i = (covariance of all classes are identical but arbitrary!) Hyperplane separating R i and R j (the hyperplane separating R i and R j is generally not orthogonal to the line between the means!)

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44 Case i = arbitrary The covariance matrices are different for each category (Hyperquadrics which are: hyperplanes, pairs of hyperplanes, hyperspheres, hyperellipsoids, hyperparaboloids, hyperhyperboloids)

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47 Bayes Decision Theory – Discrete Features Components of x are binary or integer valued, x can take only one of m discrete values v 1, v 2, …, v m Case of independent binary features in 2 category problem Let x = [x 1, x 2, …, x d ] t where each x i is either 0 or 1, with probabilities: p i = P(x i = 1 | 1 ) q i = P(x i = 1 | 2 )

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48 The discriminant function in this case is:

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