Tensor Voting based Lazy Learner for Pattern Classification

Mandar Kulkarni, Arun Kumar Mani, and Shankar M. Venkatesan

Keywords

Tensor Voting, Pattern Classification, Instancebased Learner, Lazy Learner

Abstract

In this paper, we propose a lazy learning classifier based on tensor voting framework for supervised binary and multiclass problems. Unlike other lazy learners, our approach communicates votes as tensors which allow them to communicate more information about the local structure/orientation. Hence, classification of a new datapoint is not only based on its proximity to training datapoints but also its structural alignment. The only variable parameter in our approach is the scale of voting. Our experiments on benchmark datasets demonstrate the efficacy of the proposed approach.

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