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arXiv · 1507.00019

Representing data by sparse combination of contextual data points for classification

Jingyan Wang·Yihua Zhou·Ming Yin·Shaochang Chen·Benjamin Edwards
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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.

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Jingyan Wang, Yihua Zhou, Ming Yin, Shaochang Chen, Benjamin Edwards. 2015-06-30. Representing data by sparse combination of contextual data points for classification. https://arxiv.org/abs/1507.00019

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