Two-phase probabilistic collaborative representation-based classification

Jianping Gou

doi:10.1016/j.eswa.2019.05.009

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Two-phase probabilistic collaborative representation-based classification

2019-11-01

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DOI：10.1016/j.eswa.2019.05.009

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Abstract

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The probabilistic collaborative representation-based classification (PCRC), as a novel extension of collaborative representation-based classification (CRC), is a promising method in pattern recognition. In this article, we adopt the coarse to fine representation to propose two-phase probabilistic collaborative representation based-classification (TPCRC) to enhance the power of pattern discrimination in PCRC. In TPCRC, the first phase is to utilize probabilistic collaborative representation to coarsely choose the nearest representative samples, and the second phase is to use the chosen nearest samples to finely represent and classify each testing sample. In order to employ the locality of data to further improve classification performance of PCRC, we also propose two-phase weighted probabilistic collaborative representation based classification (TWPCRC). In the fine representation of TWPCRC, the probabilistic collaborative coefficients are weighted by the local distance similarities between each testing sample and all the training samples. The proposed methods are verified by the comparative experiments on three public image data sets. Experimental results show that the proposed methods outperform the state-of-the-art collaborative representation-based classification methods. (C) 2019 Elsevier Ltd. All rights reserved.

Keywords:

Collaborative representation

Probabilistic collaborative representation

Representation-Based classification

Pattern recognition

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