Representing data by sparse combination of contextual data points for classification

TL;DR

This paper introduces a novel method for data classification that represents each data point as a sparse combination of its contextual points, jointly learning the context and classifier to improve discriminative power.

Contribution

It proposes a unified formulation for learning sparse contextual representations and classifiers simultaneously, enhancing classification accuracy over existing context-based methods.

Findings

Outperforms state-of-the-art context-based classification methods on benchmark datasets.

Effectively learns sparse contextual representations that improve discriminative ability.

Demonstrates the advantage of joint context and classifier learning in classification tasks.

Abstract

In this paper, we study the problem of using contextual da- ta points of a data point for its classification problem. We propose to represent a data point as the sparse linear reconstruction of its context, and learn the sparse context to gather with a linear classifier in a su- pervised way to increase its discriminative ability. We proposed a novel formulation for context learning, by modeling the learning of context reconstruction coefficients and classifier in a unified objective. In this objective, the reconstruction error is minimized and the coefficient spar- sity is encouraged. Moreover, the hinge loss of the classifier is minimized and the complexity of the classifier is reduced. This objective is opti- mized by an alternative strategy in an iterative algorithm. Experiments on three benchmark data set show its advantage over state-of-the-art context-based data representation and classification methods.