Graph regularized non-negative matrix factorization by maximizing correntropy. arXiv preprint arXiv:1405.2246, 2014.Li, L., Yang, J., Zhao, K., Xu, Y., Zhang, H., Fan, Z.: Graph regularized non-negative matrix factorization by maximizing correntropy. arXiv preprint arXiv:1405.2246 (2014)...
Hidru, D., Goldenberg, A.: Equinmf: Graph regularized multiview nonnegative matrix factorization. arXiv preprint arXiv:1409.4018 (2014)D. Hidru and A. Goldenberg, "Equinmf: Graph regularized multi- view nonnegative matrix factorization," Computer Science, 2014....
In this paper, we propose a new novel framework of Evolutionary Clustering based on Graph regularized Nonnegative Matrix Factorization (ECGNMF), to detect dynamic communities and the evolution patterns and predict the varying structure across the temporal networks. More concretely, we construct a ...
Given a data matrix X ? ?xij ? 2 IRM ?N , our GNMF aims to find two nonnegative matrices U ? ?uik ? 2 IRM ?K and 2. : CAI ET AL.: GRAPH REGULARIZED NONNEGATIVE MATRIX FACTORIZATION FOR DATA REPRESENTATION 1551 V ? ?vjk ? 2 IRN ?K . Similarly to NMF, we can also use ...
Graph regularized Non-negative Matrix Factorization (GNMF) algorithm which avoids this limitation by incorporating a geometrically based regularizer. 3.1 The Objective Function Recall that NMF tries to find a basis that is optimized for the linear approximation of the data which are drawn according ...
We further propose two refined-graph regularized nonnegative matrix factorization methods and use them to perform image clustering. Experimental results on several image datasets reveal that they outperform 11 representative methods.XUELONG LIChinese Academy of SciencesGUOSHENG CUI...
Thus a Graph Regularized version of NMF is needed. In this paper, we propose a Graph Regularized Non-negative Matrix Factorization (GRNMF) with emphasizing graph regularized on error function to extract characteristic gene set. This method considers the samples in low-dimensional manifold which ...
In this paper, we discuss multi-view clustering based on graph-regularized nonnegative matrix factorization with fusing useful information effectively to improve recognition accuracy. Useful information is enhanced via graph embedding, and redundant information is removed using the orthogonal constraint in ...
Graph Regularized Nonnegative Matrix Factorization for Data Representation Deng Cai Xiaofei He Jiawei...
which may limit its ability to learn higher level and more complex hierarchical information. To overcome this short- coming, in this paper, we propose a multi-view clustering method based on deep graph regularized non- negative matrix factorization (MvDGNMF). MvDGNMF is able to extract more abs...