Graph Regularized Semi-Supervised Concept Factorization

Yu Qing Shi; Shi Qiang Du; Wei Lan Wang

doi:10.4028/www.scientific.net/AEF.6-7.583

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HomeAdvanced Engineering ForumAdvanced Engineering Forum Vols. 6-7Graph Regularized Semi-Supervised Concept...

Graph Regularized Semi-Supervised Concept Factorization

Abstract:

Concept Factorization (CF) is a new matrix decomposition technique for data representation. A modified CF algorithm called Graph Regularized Semi-supervised Concept Factorization (GRSCF) is proposed for addressing the limitations of CF and Local Consistent Concept Factorization (LCCF), which did not consider the geometric structure or the label information of the data. GRSCF preserves the intrinsic geometry of data as regularized term and use the label information as semi-supervised learning, it makes nearby samples with the same class-label are more compact, and nearby classes are separated. Compared with Non-Negative Matrix Factorization (NMF), CNMF, CF and LCCF, experiment results on ORL face database and Coil20 image database have shown that the proposed method achieves better clustering results.

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Periodical:

Advanced Engineering Forum (Volumes 6-7)

Pages:

583-588

DOI:

https://doi.org/10.4028/www.scientific.net/AEF.6-7.583

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Online since:

September 2012

Authors:

Yu Qing Shi, Shi Qiang Du, Wei Lan Wang

Keywords:

Concept Factorization (CF), Graph Regularized, Image Clustering, Semi-Supervised Learning

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Creative Commons CC BY 4.0

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