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Supervised and Constrained Nonnegative Matrix Factorization with Sparseness for Image Representation

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Abstract

Nonnegative Matrix Factorization (NMF) has been becoming an important method for feature dimension reduction in recent years since it can represent data with strong strength. In this paper, we present a novel image representation method by integrating prior information of known samples and a certain sparseness constraint into NMF framework simultaneously, called Supervised and Constrained Nonnegative Matrix Factorization with Sparseness (SCNMFS). The proposed method can harvest the promising identification ability of the acquired coefficient matrix under the condition that the better sparsity of the factorized base matrix is ensured. From the perspective of theory, the proposed method can effectively reveal the intrinsic geometric characteristics of images. Extensive experimental results on common benchmarks demonstrate that SCNMFS has superiority compared with three state-of-the-art algorithms in image classification problem.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou, China

    Xibiao Cai & Fuming Sun

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  1. Xibiao Cai
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  2. Fuming Sun
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Correspondence to Xibiao Cai.

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Cai, X., Sun, F. Supervised and Constrained Nonnegative Matrix Factorization with Sparseness for Image Representation. Wireless Pers Commun 102, 3055–3066 (2018). https://doi.org/10.1007/s11277-018-5325-1

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  • DOI: https://doi.org/10.1007/s11277-018-5325-1

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