Machine Learning. Probabilistic KNN.

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1 Machine Learning. Mark Girolami Department of Computing Science University of Glasgow June 21, 2007 p. 1/3

2 KNN is a remarkably simple algorithm with proven error-rates June 21, 2007 p. 2/3

3 KNN is a remarkably simple algorithm with proven error-rates One drawback is that it is not built on any probabilistic framework June 21, 2007 p. 2/3

4 KNN is a remarkably simple algorithm with proven error-rates One drawback is that it is not built on any probabilistic framework No posterior probabilities of class membership June 21, 2007 p. 2/3

5 KNN is a remarkably simple algorithm with proven error-rates One drawback is that it is not built on any probabilistic framework No posterior probabilities of class membership No way to infer number of neighbours or metric parameters probabilistically June 21, 2007 p. 2/3

6 KNN is a remarkably simple algorithm with proven error-rates One drawback is that it is not built on any probabilistic framework No posterior probabilities of class membership No way to infer number of neighbours or metric parameters probabilistically Let us try and get around this problem June 21, 2007 p. 2/3

7 The first thing which is needed is a likelihood June 21, 2007 p. 3/3

8 The first thing which is needed is a likelihood Consider a finite data sample {(t 1,x 1 ),, (t N,x N )} where each t n {1,,C} denotes the class label and D-dimensional feature vector x n R D. The feature space R D has an associated metric with parameters θ denoted as M θ. June 21, 2007 p. 3/3

9 The first thing which is needed is a likelihood Consider a finite data sample {(t 1,x 1 ),, (t N,x N )} where each t n {1,,C} denotes the class label and D-dimensional feature vector x n R D. The feature space R D has an associated metric with parameters θ denoted as M θ. A likelihood can be formed as p(t X,β,k,θ, M) n=1 exp C c=1 { exp β k { M θ j n k β k M θ j n k δ tn t j } δ ctn } June 21, 2007 p. 3/3

10 The number of nearest neighbours is k and β defines a scaling variable. The expression M θ j n k δ tn t j denotes the number of the nearest k neighbours of x n, as measured under the metric M θ within N 1 samples from X remaining when x n is removed which we denote as X n, and have the class label value of t n, whilst each of the terms in the summation of the denominator provides a count of the number of the k neighbours of x n which have class label equaling c. June 21, 2007 p. 4/3

11 Likelihood formed by product of terms p(t n x n,x n,t n,β,k,θ, M) June 21, 2007 p. 5/3

12 Likelihood formed by product of terms p(t n x n,x n,t n,β,k,θ, M) This is a Leave-One-Out (LOO) predictive likelihood, where t n denotes the vector t with the n th element removed June 21, 2007 p. 5/3

13 Likelihood formed by product of terms p(t n x n,x n,t n,β,k,θ, M) This is a Leave-One-Out (LOO) predictive likelihood, where t n denotes the vector t with the n th element removed Approximate joint likelihood provides an overall measure of the LOO predictive likelihood June 21, 2007 p. 5/3

14 Likelihood formed by product of terms p(t n x n,x n,t n,β,k,θ, M) This is a Leave-One-Out (LOO) predictive likelihood, where t n denotes the vector t with the n th element removed Approximate joint likelihood provides an overall measure of the LOO predictive likelihood Should exhibit some resiliance to overfitting due to the LOO nature of the approximate likelihood June 21, 2007 p. 5/3

15 Posterior inference will follow by obtaining the parameter posterior distribution p(β, k, θ t, X, M) June 21, 2007 p. 6/3

16 Posterior inference will follow by obtaining the parameter posterior distribution p(β, k, θ t, X, M) Predictions of the target class label t of a new datum x are made by posterior averaging such that p(t x,t,x, M) equals p(t x,t,x,β,k,θ, M)p(β,k,θ t,x, M)dβdθ k June 21, 2007 p. 6/3

17 Posterior inference will follow by obtaining the parameter posterior distribution p(β, k, θ t, X, M) Predictions of the target class label t of a new datum x are made by posterior averaging such that p(t x,t,x, M) equals p(t x,t,x,β,k,θ, M)p(β,k,θ t,x, M)dβdθ k Posterior takes an intractable form so MCMC procedure is proposed so that the following Monte-Carlo estimate is employed ˆp(t x,t,x, M) = 1 N s N s s=1 p(t x,t,x,β (s),k (s),θ (s), M) June 21, 2007 p. 6/3

18 Posterior sampling algorithm simple Metropolis algorithm June 21, 2007 p. 7/3

19 Posterior sampling algorithm simple Metropolis algorithm Assume priors on k and β are uniform over all possible values (integer & real) June 21, 2007 p. 7/3

20 Posterior sampling algorithm simple Metropolis algorithm Assume priors on k and β are uniform over all possible values (integer & real) Proposal distribution for β new is Gaussian i.e. N(β (i),h) June 21, 2007 p. 7/3

21 Posterior sampling algorithm simple Metropolis algorithm Assume priors on k and β are uniform over all possible values (integer & real) Proposal distribution for β new is Gaussian i.e. N(β (i),h) Proposal distribution for k is uniform between Min & Max values index U(0,k step + 1) k new = k old + k inc (index); June 21, 2007 p. 7/3

22 Need to accept this new move using Metropolis ratio June 21, 2007 p. 8/3

23 Need to accept this new move using Metropolis ratio min { 1, p(t X,β } new,k new,θ new, M) p(t X,β,k,θ, M) June 21, 2007 p. 8/3

24 Need to accept this new move using Metropolis ratio min { 1, p(t X,β } new,k new,θ new, M) p(t X,β,k,θ, M) Builds up a Markov Chain whose stationary distribution is p(β,k,θ t,x, M) June 21, 2007 p. 8/3

25 Need to accept this new move using Metropolis ratio min { 1, p(t X,β } new,k new,θ new, M) p(t X,β,k,θ, M) Builds up a Markov Chain whose stationary distribution is p(β,k,θ t,x, M) Very simple algorithm to implement - Matlab and C implementations available June 21, 2007 p. 8/3

26 Trace of Metropolis Sampler for β & k x x 10 4 June 21, 2007 p. 9/3

27 No. of SAMPLES K %CV ERROR K Figure 1: The top graph shows a histogram of the marginal posterior for K on the synthetic Ripley dataset and the bottom shows the 10CV error against the value of K. June 21, 2007 p. 10/3

28 Percentage Test Error PKNN KNN Size of Data Set Figure 2: The percentage test error obtained with training sets of varying size from 25 to 250 data points. For each sub-sample size, 50 random subsets were sampled and each of these used to obtain a KNN and PKNN classifier which were then used to make predictions on the 1000 independent test points. The mean percentage performance and associated standard error obtained for each training set are shown in the above figure for each classifier. June 21, 2007 p. 11/3

29 Data KNN PKNN P-Value Glass ± ± Iris 5.33 ± ± Crabs ± ± Pima ± ± Soybean ± ± Wine ± ± Balance ± ± Heart ± ± Liver ± ± Diabetes ± ± Vehicle ± ± June 21, 2007 p. 12/3

30 Data KNN PKNN Glass Iris Crabs Pima Soybean Wine Balance Heart Liver Diabetes Vehicle June 21, 2007 p. 13/3

31 PKNN is a fully Bayesian method for KNN classification June 21, 2007 p. 14/3

32 PKNN is a fully Bayesian method for KNN classification Requires MCMC therefore slow June 21, 2007 p. 14/3

33 PKNN is a fully Bayesian method for KNN classification Requires MCMC therefore slow Possible to learn metric though this is computationally demanding June 21, 2007 p. 14/3

34 PKNN is a fully Bayesian method for KNN classification Requires MCMC therefore slow Possible to learn metric though this is computationally demanding Predictive probabilities more useful in certain applications - e.g. clinical prediction June 21, 2007 p. 14/3

35 PKNN is a fully Bayesian method for KNN classification Requires MCMC therefore slow Possible to learn metric though this is computationally demanding Predictive probabilities more useful in certain applications - e.g. clinical prediction On 0-1 loss no statistically significant difference with CV & KNN June 21, 2007 p. 14/3

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