To improve the robustness of NMF, a novel algorithm named robust nonnegative matrix factorization (RNMF) is proposed in this paper. We assume that some entries of the data matrix may be arbitrarily corrupted, but the corruption is sparse. RNMF decomposes the non-negative data matrix as the ...
Liu, "Robust nonnegative matrix factoriza- tion via 1 norm regularization," arXiv preprint arXiv:1204.2311, 2012.B. Shen, B. Liu, Q. Wang, and R. Ji, "Robust nonnegative matrix factorization via l1 norm regularization by multiplicative updating rules," in Proc. ICIP, Paris, France, Oct...
Generalized alpha-beta diver- gences and their application to robust nonnegative matrix factorization. En- tropy 13 (1), 134-170.A. Cichocki, S. Cruces, and S.-i. Amari. Generalized alpha-beta divergences and their application to robust nonnegative matrix factorization. Entropy, 13(1):134-...
In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l(2,1)-norm and the non-negative constraints not only removes ...
Non-negative matrix factorization (NMF) and its variants have been widely employed in clustering and classification task. However, the existing methods do not consider robustness, adaptive graph learning and discrimination information at the same time. To solve this problem, a new nonnegative matrix ...
Robust nonnegative matrix factorization with structure regularization Nonnegative matrix factorization (NMF) has attracted more and more attention due to its wide applications in computer vision, information retrieval, and ma... Q Huang,X Yin,S Chen,... - 《Neurocomputing》 被引量: 0发表: 2020...
A new unsupervised feature selection method, i.e., Robust Unsupervised Feature Selection (RUFS), is proposed. Unlike traditional unsupervised feature selection methods, pseudo cluster labels are learned via local learning regularized robust nonnegative matrix factorization. During the label learning process...
Robust self supervised symmetric nonnegative matrix factorization to the graph clustering ArticleOpen access01 March 2025 Degree difference: a simple measure to characterize structural heterogeneity in complex networks Introduction Almost everything in our daily life can be modeled as complex networks, such...
Peng S, Ser W, Chen B, Lin Z (2021) Robust semi-supervised nonnegative matrix factorization for image clustering. Pattern Recognition 111:107683 Google Scholar Cai D, Zhang C, He X (2010) Unsupervised feature selection for multi-cluster data. In: Proceedings of the 16th ACM SIGKDD interna...
Python PyTorch (GPU) and NumPy (CPU)-based port of Févotte and Dobigeon's robust-NMF algorithm appearing in "Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization." pytorch matrix-factorization non-negative-matrix-factorization robust-statistics robust-nmf Updated on Sep 1, ...