Graph Regularized Non-Negative Low-Rank Matrix Factorization for Image Clustering

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Abstract
Non-negative matrix factorization (NMF) has been one of the most popular methods for feature learning in the field of machine learning and computer vision. Most existing works directly apply NMF on high-dimensional image datasets for computing the effective representation of the raw images. However, in fact, the common essential information of a given class of images is hidden in their low rank parts. For obtaining an effective low-rank data representation, we in this paper propose a non-negative low-rank matrix factorization (NLMF) method for image clustering. For the purpose of improving its robustness for the data in a manifold structure, we further propose a graph regularized NLMF by incorporating the manifold structure information into our proposed objective function. Finally, we develop an efficient alternating iterative algorithm to learn the low-dimensional representation of low-rank parts of images for clustering. Alternatively, we also incorporate robust principal component analysis into our proposed scheme. Experimental results on four image datasets reveal that our proposed methods outperform four representative methods.
Funding Information
  • National Natural Science Foundation of China (61125106, 61301230)
  • International Science and Technology Cooperation Project of Henan Province (162102410021)
  • China Post-Doctoral Science Foundation (2014M550517, 2015T81063)
  • Key Research Program of the Chinese Academy of Sciences (KGZD-EW-T03)
  • State Key Laboratory of Virtual Reality Technology and Systems (BUAA-VR-16KF-04)
  • Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province (GD201605)

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