Classification Discriminant Analysis

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Presentation transcript:

Classification Discriminant Analysis slides thanks to Greg Shakhnarovich (CS195-5, Brown Univ., 2006)

Example of applying Fisher’s LDA maximize separation of means maximize Fisher’s LDA criterion  better class separation

Using LDA for classification in one dimension Fisher’s LDA gives an optimal choice of w, the vector for projection down to one dimension. For classification, we still need to select a threshold to compare projected values to. Two possibilities: No explicit probabilistic assumptions. Find threshold which minimizes empirical classification error. Make assumptions about data distributions of the classes, and derive theoretically optimal decision boundary. Usual choice for class distributions is multivariate Gaussian. We also will need a bit of decision theory.

Decision theory To minimize classification error: At a given point x in feature space, choose as the predicted class the class that has the greatest probability at x.

Decision theory At a given point x in feature space, choose as the predicted class the class that has the greatest probability at x. probability densities for classes C1 and C2 relative probabilities for classes C1 and C2

MATLAB interlude Classification via discriminant analysis, using the classify() function. Data for each class modeled as multivariate Gaussian. matlab_demo_06.m class = classify( sample, training, group, ‘type’ ) predicted test labels test data training data training labels model for class covariances

MATLAB classify() function Models for class covariances ‘linear’: all classes have same covariance matrix  linear decision boundary ‘diaglinear’: all classes have same diagonal covariance matrix  linear decision boundary ‘quadratic’: classes have different covariance matrices  quadratic decision boundary ‘diagquadratic’: classes have different diagonal covariance matrices  quadratic decision boundary

MATLAB classify() function Example with ‘quadratic’ model of class covariances