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Tensor Voting Based Binary Classifier

Authors

Mandar S. Kulkarni, Shankar M. Venkatesan and M. Arunkumar, Philips Research India, India

Abstract

We propose two novel Tensor Voting (TV) based supervised binary classification algorithms for N-Dimensional (N-D) data points. (a) The first one finds an approximation to a separating hyper-surface that best separates the given two classes in N-D: this is done by finding a set of candidate decision-surface points (using the training data) and then modeling the decision surface by local planes using N-D TV; test points are then classified based on local plane equations. (b) The second algorithm defines a class similarity measure for a given test point t, which is the maximum over all inner products of the vector from t (to training point p) and the tangent at p (computed with TV): t is then assigned the class with the best similarity measure. Our approach is fast, local in nature and is equally valid for different kinds of decisions: we performed several experiments on real and synthetic data to validate our approach, and compared our approaches with standard classifiers such as kNN and Decision Trees.

Keywords

Full Text  Volume 3, Number 5