1. 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

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

3. Pattern Classification, Chapter 3 2 l Introduction l Data availability in a Bayesian framework l We could design an optimal classifier if we knew: l P(  i ) (priors) l P(x |  i ) (class-conditional densities) Unfortunately, we rarely have this complete information! l Design a classifier from a training sample l No problem with prior estimation l Samples are often too small for class-conditional estimation (large dimension of feature space!) 1

4. Pattern Classification, Chapter 3 3 l A priori information about the problem l Do we know something about the distribution? l  find parameters to characterize the distribution l Example: Normality of P(x |  i ) P(x |  i ) ~ N(  i,  i ) l Characterized by 2 parameters l Estimation techniques l Maximum-Likelihood (ML) and the Bayesian estimations l Results are nearly identical, but the approaches are different 1

5. Pattern Classification, Chapter 3 4 l Parameters in ML estimation are fixed but unknown! l Best parameters are obtained by maximizing the probability of obtaining the samples observed l Bayesian methods view the parameters as random variables having some known distribution l In either approach, we use P(  i | x) for our classification rule! 1

6. Pattern Classification, Chapter 3 5 l Maximum-Likelihood Estimation l Has good convergence properties as the sample size increases l Simpler than any other alternative techniques l General principle l Assume we have c classes and P(x |  j ) ~ N(  j,  j ) P(x |  j )  P (x |  j,  j ) where: 2

7. Pattern Classification, Chapter 3 6 l Use the information provided by the training samples to estimate  = (  1,  2, …,  c ), each  i (i = 1, 2, …, c) is associated with each category l Suppose that D contains n samples, x 1, x 2,…, x n l 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

8. Pattern Classification, Chapter 3 7 2

9. 8 l Optimal estimation l Let  = (  1,  2, …,  p ) t and let   be the gradient operator l We define l(  ) as the log-likelihood function l(  ) = ln P(D |  ) (recall D is the training data) l New problem statement: determine  that maximizes the log-likelihood 2

10. Pattern Classification, Chapter 3 9 The definition of l() is: and Set of necessary conditions for an optimum is:   l = 0 (eq. 7) 2

11. Pattern Classification, Chapter 3 10 l Example, the Gaussian case: unknown  l We assume we know the covariance l p(x i |  ) ~ N( ,  ) (Samples are drawn from a multivariate normal population)  =  therefore: The ML estimate for  must satisfy: 2

12. Pattern Classification, Chapter 3 11 Multiplying by  and rearranging, we obtain: Just the arithmetic average of the samples of the training samples! Conclusion: If P(x k |  j ) (j = 1, 2, …, c) is supposed to be Gaussian in a d- dimensional feature space; then we can estimate the vector  = (  1,  2, …,  c ) t and perform an optimal classification! 2

13. Pattern Classification, Chapter 3 12 Example, Gaussian Case: unknown  and  l First consider univariate case: unknown  and   = (  1,  2 ) = ( ,  2 ) 2

14. Pattern Classification, Chapter 3 13 Summation (over the training set): Combining (1) and (2), one obtains: 2

15. Pattern Classification, Chapter 3 14 l The ML estimates for the multivariate case is similar The scalars  and  are replaced with vectors The variance  2 is replaced by the covariance matrix

16. Pattern Classification, Chapter 3 15 Bias l ML estimate for  2 is biased l Extreme case: n=1, E[ ] = 0 ≠  2 l As the n increases the bias is reduced  this type of estimator is called asymptotically unbiased 2

17. Pattern Classification, Chapter 3 16 l An elementary unbiased estimator for  is: This estimator is unbiased for all distributions  Such estimators are called absolutely unbiased 2

18. Pattern Classification, Chapter 3 17 l Our earlier estimator for  is biased: In fact it is asymptotically unbiased: Observe that 2

19. Pattern Classification, Chapter 3 18 l Appendix: ML Problem Statement l Let D = {x 1, x 2, …, x n } P(x 1,…, x n |  ) =  1,n P(x k |  ); |D| = n Our goal is to determine (value of  that maximizes the likelihood of this sample set!) 2

20. Pattern Classification, Chapter 3 19 |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 |  1 ) P(x j |  k ) 2

21. Pattern Classification, Chapter 3 20  = (  1,  2, …,  c ) Problem: find such that: 2