Non-negative low rank and sparse graph for semi-supervised learning
Non-negative low rank and sparse graph for semi-supervised learning
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DOI：
10.1109/cvpr.2012.6247944
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发表时间：
2012-06
期刊：
影响因子：
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通讯作者：
Liansheng Zhuang;Haoyuan Gao;Zhouchen Lin;Yi Ma;Xin Zhang;Nenghai Yu
中科院分区：
文献类型：
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作者：
Liansheng Zhuang;Haoyuan Gao;Zhouchen Lin;Yi Ma;Xin Zhang;Nenghai Yu
构建一个良好的图来表示数据结构对于许多重要的机器学习任务（如聚类和分类）至关重要。提出了一种新的用于半监督学习的非负低秩稀疏图（NNLRS）。图中的边的权重通过寻找一个非负的低秩和稀疏矩阵来获得，该矩阵将每个数据样本表示为其他数据样本的线性组合。这样得到的NNLRS-图既可以捕捉到数据的全局混合子空间结构（通过低秩），又可以捕捉到数据的局部线性结构（通过稀疏性），因此它既具有生成性又具有判别性。我们证明了NNLRS-图在半监督分类和判别分析中的有效性。大量的实验证明了NNLRS-图的显着优势，通过传统的手段获得的图。
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.