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Pattern Classification & Decision Theory. How are we doing on the pass sequence? Bayesian regression and estimation enables us to track the man in the.

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Presentation on theme: "Pattern Classification & Decision Theory. How are we doing on the pass sequence? Bayesian regression and estimation enables us to track the man in the."— Presentation transcript:

1 Pattern Classification & Decision Theory

2 How are we doing on the pass sequence? Bayesian regression and estimation enables us to track the man in the striped shirt based on labeled data Can we track the man in the white shirt? Not very well. Feature, x Hand-labeled horizontal coordinate, t Regression fails to identify that there really are two classes of solution

3 Decision theory We wish to classify an input x as belonging to one of K classes, where class k is denoted Example: Buffalo digits, 10 classes, 16x16 images, each image x is a 256-dimensional vector, x m [0,1]

4 Decision theory We wish to classify an input x as belonging to one of K classes, where class k is denoted Partition the input space into regions,, …, so that if x, our classifier predicts class How should we choose the partition? Suppose x is presented with probability p(x) and the distribution over the class labels given x is p( |x) Then, p(correct) = k x p(x) p( |x) dx This is maximized by assigning each x to the region whose class maximizes p( |x)

5 Three approaches to pattern classification 1.Discriminative and non-probabilistic –Learn a discriminant function f (x), which maps x directly to a class label 2.Discriminative and probabilistic –For each class k, learn the probability model –Use this probability to classify a new input x f(x)f(x)

6 Three approaches to pattern classification 3.Generative –For each class k, learn the generative probability model –Also, learn the class probabilities –Use Bayes rule to classify a new input: where

7 Three approaches to pattern classification 1.Discriminative and non-probabilistic –Learn a discriminant function f (x), which maps x directly to a class label 2.Discriminative and probabilistic –For each class k, learn the probability model –Use this probability to classify a new input x f(x)f(x)

8 Can we use regression to learn discriminant functions? f(x)f(x)

9 f(x)f(x) f(x)f(x) What do the classification regions look like? Is there any sense in which square error is an appropriate cost function for classification? We should be careful to not interpret integer-valued class labels as ordinal targets for regression Can we use regression to learn discriminant functions?

10 The one-of-K representation For > 2 classes, each class is represented by a binary vector t with 1 indicating the class: K regression problems: To classify x, pick class k with largest y k (x) t =

11 Lets focus on binary classification Predict target t {0,1} from input vector x Denote the m th input of training case n by x nm

12 Classification boundary for linear regression Values of x where y(x)=0.5 are ambiguous – these form the classification boundary For these points, = 0.5 If x is ( M+1 )-dimensional, this defines an M - dimensional hyperplane separating the classes

13 How well does linear regression work? Works well in some cases, but there are two problems: Due to linearity, extreme x s cause extreme y(x,w) s Due to squared error, extreme y(x,w) s dominate learning Logistic regression (more later) Linear regression

14 Clipping off extreme y s To clip off extremes of, we can use a sigmoid function: where Now, squared error wont penalize extreme x s y is now a non-linear function of w, so learning is harder

15 How the observed error propagates back to the parameters E(w) = ½ n ( t n – ( m w m x nm ) ) 2 The rate of change of E w.r.t. w m is E(w)/ w m = - n ( t n - y n ) y n ( 1 - y n ) x nm –Useful fact:(a) = (a) ( 1 - (a)), Compare with linear regression: E(w)/ w m = - n ( t n - y n ) x nm ynyn

16 The effect of clipping Regression with sigmoid: E(w)/ w m = - n ( t n - y n ) y n ( 1 - y n ) x nm Linear regression: E(w)/ w m = - n ( t n - y n ) x nm For these outliers, both (t n -y n ) 0 and y( 1 -y) 0, so the outliers wont hold back improvement of the boundary

17 Squared error for learning probabilities If t = 0 and y 1, y is moderately pulled down (grad 1 ) If t = 0 and y 0, y is weakly pulled down (grad 0 ) E = ½ (t-y) 2 y t = 0t = 0 E dE/dy = 1 dE/dy = 0 Problems: Being certainly wrong is often undesirable Often, tiny differences between small probabilities count a lot

18 Three approaches to pattern classification 1.Discriminative and non-probabilistic –Learn a discriminant function f (x), which maps x directly to a class label 2.Discriminative and probabilistic –For each class k, learn the probability model –Use this probability to classify a new input x f(x)f(x)

19 Logistic regression: Binary likelihood As before, we use: where Now, use binary likelihood: p(t|x) = y(x) t (1-y(x)) 1 -t Data log-likelihood: L = n t n ln ( m w m x nm ) + ( 1 -t n ) ln ( 1- ( m w m x nm ) ) Unlike linear regression, L is nonlinear in the w s, so gradient-based optimizers are needed

20 Binary likelihood for learning probabilities If t = 0 and y 1, y is strongly pulled down (grad ) If t = 0 and y 0, y is moderately pulled down (grad 1 ) E = -ln(1-y) y t = 0t = 0 E dE/dy = 1 dE/dy = 0 E = ½ (t-y) 2 t = 0t = 0 dE/dy = 1 dE/dy

21 How the observed error propagates back to the parameters L = n t n ln ( m w m x nm ) + ( 1 -t n ) ln ( 1- ( m w m x nm ) ) The rate of change of L w.r.t. w m is L / w m = n ( t n - y n ) x nm Compare with sigmoid plus squared error: E(w)/ w m = - n ( t n - y n ) y n ( 1 - y n ) x nm Compare with linear regression: E(w)/ w m = - n ( t n - y n ) x nm ynyn

22 How well does logistic regression work? Logistic regression Linear regression

23 Multiclass logistic regression Create one set of weights per class and define The K -class generalization of the sigmoid function is p(t|x) = k exp(t k y k (x)) / k exp(y k (x)) which is equivalent to p( |x) = exp(y k (x)) / j exp(y j (x)) Learning: Similar to logistic regression (see textbook)

24 Three approaches to pattern classification 3.Generative –For each class k, learn the generative probability model –Also, learn the class probabilities –Use Bayes rule to classify a new input: where

25 Gaussian generative models We can assume each element of x is independent and Gaussian, given the class: p(x| ) = m p(x m | ) = m N (x m | km, km 2 ) Contour plot of density: k1 k2 k1 k2 Isotropic Gaussian: k1 2 = k2 2

26 Learning a Buffalo digit classifier (5000 training cases) The generative ML estimates of km and km 2 are just the data means and variances: Means: Variances (black=low variance, white=high variance): The classes are equally frequent, so = 1/10 To classify a new input x, compute (in the log-domain!)

27 A problem with the ML estimate Some pixels were constant across all training images within a class, so ML 2 = 0 This causes numerical problems when evaluating Gaussian densities, but is also an overfitting problem Common hack: Add min 2 to all variances More principled approaches –Regularize 2 –Place a prior on 2 and use MAP –Place a prior on 2 and use Bayesian learning

28 Classifying test data (5000 test cases) Adding min 2 = 0.01 to the variances, we obtain: –Error rate on training set = 16.00% (std dev.5%) –Error rate on test set = 16.72% (std dev.5%) Effect of value of min 2 on error rates: log 10 min 2 Test error rate Training error rate

29 Full-covariance Gaussian models Let x = y, where y is isotropic Gaussian and is an M x M rotation and scale matrix This generates a full-covariance Gaussian: Defining = ( -1 T -1 ) - 1, we obtain where is the covariance matrix: jk = COV(x j, x k ) Determinant

30 Generative models easily induce non-linear decision boundaries The following three-class problem shows how three axis-aligned Gaussians can induce nonlinear decision boundaries

31 Generative models easily induce non-linear decision boundaries Two Gaussians can be used to account for inliers and outliers

32 How are we doing on the pass sequence? Bayesian regression and estimation enables us to track the man in the striped shirt based on labeled data Can we track the man in the white shirt? Not very well. Feature, x Hand-labeled horizontal coordinate, t Regression fails to identify that there really are two classes of solution

33 Position of man in white shirt Using classification to improve tracking The position of the man in the striped shirt can be used to classify the tracking mode of the man in the white shirt Feature Position of man in striped shirt Feature

34 Position of man in white shirt, t w Using classification to improve tracking x s and t s = feature and position of man in striped shirt x w and t w = feature and position of man in white shirt For man in white shirt, hand-label regions and and learn two trackers Feature, x s Position of man in striped shirt, t s Feature, x w p( |t s ) p(t w |x w, )p(ts|xs)p(ts|xs) p( |t s ) p(t w |x w, )

35 Using classification to improve tracking The classifier can be obtained using the generative approach, where each class-conditional likelihood is a Gaussian Note: Linear classifiers wont work Feature, x s Position of man in striped shirt, t s p( |t s ) p(ts|xs)p(ts|xs) p(t s | )

36 Questions?

37 How are we doing on the pass sequence? We can now track both men, provided with –Hand-labeled coordinates of both men in 30 frames –Hand-extracted features (stripe detector, white blob detector) –Hand-labeled classes for the white-shirt tracker We have a framework for how to optimally make decisions and track the men

38 How are we doing on the pass sequence? We can now track both men, provided with –Hand-labeled coordinates of both men in 30 frames –Hand-extracted features (stripe detector, white blob detector) –Hand-labeled classes for the white-shirt tracker We have a framework for how to optimally make decisions and track the men This takes too much time to do by hand!

39

40 Lecture 4 Appendix

41 Binary classification regions for linear regression is defined by, and vice versa for Values of x satisfying are on the decision boundary, which is a D-1 dimensional hyperplane –w specifies the orientation of the decision hyperplane –-w 0 /||w|| specifies the distance from the hyperplane to the origin –The distance from input x to the hyperplane is y(x)/||w||

42 K -ary classification regions for linear regression x if Each resulting classification region is contiguous and has linear boundaries:

43 Fishers linear discriminant and least squares Fisher: Viewing y = w T x as a projection, pick w to maximize the distance between the means of the data sets, while also minimizing the variances of the data sets This result is also obtained using linear regression, by setting t = N/N 1 for class 1 and t = - N/N 2 for class 2, where N k = # training cases in class k

44 In what sense is logistic regression linear? The log-odds can be written thus: Each input contributes linearly to the log-odds 1-

45 Gaussian likelihoods and logistic regression For two classes, if their covariance matrices are equal, 1 = 2 =, we can write the log-odds as So… = = = where Logistic regression classifiers Classifiers using equal-covariance Gaussian generative models

46 Linear models or classifiers with linear boundaries Dont be fooled Such classifiers can be very hard to learn Such classifiers may have boundaries that are highly nonlinear in x (eg, via basis functions) All this means is that in some space the boundaries are linear


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