Supervised learning of sparse context reconstruction coefficients for data representation and classification
Abstract
Context of data points, which is usually defined as the other data points in a data set, has been found to play important roles in data representation and classification. In this paper, we study the problem of using context of a data point for its classification problem. Our work is inspired by the observation that actually only very few data points are critical in the context of a data point for its representation and classification. We propose to represent a data point as the sparse linear combination of its context, and learn the sparse context in a supervised way to increase its discriminative ability. To this end, we proposed a novel formulation for context learning, by modeling the learning of context parameter and classifier in a unified objective, and optimizing it with 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.
Cite
@article{arxiv.1508.04221,
title = {Supervised learning of sparse context reconstruction coefficients for data representation and classification},
author = {Xuejie Liu and Jingbin Wang and Ming Yin and Benjamin Edwards and Peijuan Xu},
journal= {arXiv preprint arXiv:1508.04221},
year = {2015}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1507.00019