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Manifold regularized non-negative matrix factorization with label information

[+] Author Affiliations
Huirong Li

Xi’an Jiaotong University, The School of Mathematics and Statistics, Xi’an 710049, China

Shangluo University, The School of Mathematics and Computer Application, Shang Luo 726000, China

Jiangshe Zhang, Changpeng Wang, Junmin Liu

Xi’an Jiaotong University, The School of Mathematics and Statistics, Xi’an 710049, China

J. Electron. Imaging. 25(2), 023023 (Apr 19, 2016). doi:10.1117/1.JEI.25.2.023023
History: Received November 11, 2015; Accepted March 15, 2016
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Abstract.  Non-negative matrix factorization (NMF) as a popular technique for finding parts-based, linear representations of non-negative data has been successfully applied in a wide range of applications, such as feature learning, dictionary learning, and dimensionality reduction. However, both the local manifold regularization of data and the discriminative information of the available label have not been taken into account together in NMF. We propose a new semisupervised matrix decomposition method, called manifold regularized non-negative matrix factorization (MRNMF) with label information, which incorporates the manifold regularization and the label information into the NMF to improve the performance of NMF in clustering tasks. We encode the local geometrical structure of the data space by constructing a nearest neighbor graph and enhance the discriminative ability of different classes by effectively using the label information. Experimental comparisons with the state-of-the-art methods on theCOIL20, PIE, Extended Yale B, and MNIST databases demonstrate the effectiveness of MRNMF.

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Topics

Databases ; Matrices

Citation

Huirong Li ; Jiangshe Zhang ; Changpeng Wang and Junmin Liu
"Manifold regularized non-negative matrix factorization with label information", J. Electron. Imaging. 25(2), 023023 (Apr 19, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.2.023023


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