Supervised learning of sparse context reconstruction coefficients for data representation and classification. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Liu, Xuejie; Wang, Jingbin; Yin, Ming; Edwards, Benjamin; Xu, Peijuan
• Is Part Of: Neural computing & applications. Volume 28:Issue 1(2017)
• Page Start: 135
• Page End: 143
• Publication Date: 2017-01
• Subjects: Pattern classification -- Data representation -- Context -- Nearest neighbors -- Sparse regularization; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2042-5
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Context of data points, which is usually defined as the other data points in a data set, has been found to paly 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.