2012 IEEE Conference on Computer Vision and Pattern Recognition (2012)
Providence, RI USA
June 16, 2012 to June 21, 2012
Xin Zhang , Dept. of Comput. Sci. & Tech., Tsinghua Univ., Beijing, China
Yi Ma , Microsoft Res. Asia, Beijing, China
Zhouchen Lin , Microsoft Res. Asia, Beijing, China
Haoyuan Gao , MOE-Microsoft Key Lab., Univ. of Sci. & Technol. of China, Hefei, China
Liansheng Zhuang , MOE-Microsoft Key Lab., Univ. of Sci. & Technol. of China, Hefei, China
Nenghai Yu , MOE-Microsoft Key Lab., Univ. of Sci. & Technol. of China, Hefei, China
Constructing a good graph to represent data structures is critical for many important machine learning tasks such as clustering and classification. This paper proposes a novel non-negative low-rank and sparse (NNLRS) graph for semi-supervised learning. The weights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse matrix that represents each data sample as a linear combination of others. The so-obtained NNLRS-graph can capture both the global mixture of subspaces structure (by the low rankness) and the locally linear structure (by the sparseness) of the data, hence is both generative and discriminative. We demonstrate the effectiveness of NNLRS-graph in semi-supervised classification and discriminative analysis. Extensive experiments testify to the significant advantages of NNLRS-graph over graphs obtained through conventional means.
pattern clustering, data structures, graph theory, learning (artificial intelligence), matrix algebra, pattern classification, discriminative analysis, nonnegative low rank-and-sparse graph, NNLRS-graph, semisupervised learning, data structure representation, machine learning tasks, clustering task, nonnegative low-rank-and-sparse matrix, subspaces structure, locally linear structure, semisupervised classification, Databases, Sparse matrices, Noise, Strontium, Vectors, Optimization, Educational institutions
Xin Zhang, Yi Ma, Zhouchen Lin, Haoyuan Gao, Liansheng Zhuang and Nenghai Yu, "Non-negative low rank and sparse graph for semi-supervised learning," 2012 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Providence, RI USA, 2012, pp. 2328-2335.