Robust non-negative matrix factorization via joint sparse and graph regularization

Shizhun Yang,Changshui Zhang,Shifeng Weng,Yi Wu,Chenping Hou

doi:10.1109/ijcnn.2013.6706859

Robust non-negative matrix factorization via joint sparse and graph regularization

Shizhun Yang, Changshui Zhang + Show 3 more

https://doi.org/10.1109/ijcnn.2013.6706859

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Publication Date: Aug 1, 2013

Citations: 7

Affiliation: Tsinghua University, Zhejiang Wanli University, National University of Defense Technology

#Robust Non-negative Matrix Factorization #Joint Sparse Regularization + Show 8 more

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Abstract

In real world applications, we often have to deal with some high-dimensional, sparse and noisy data. In this paper, we aim to handle this kind of complex data by a Robust Non-negative Matrix Factorization via joint Sparse and Graph regularization model (RSGNMF). We provide a novel efficient and elegant iterative updating algorithm with rigorous convergence analysis for RSGNMF model. Experimental results on image data sets demonstrate that our RSGNMF model outperforms existing start-of-art methods.

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