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- W4320060390 abstract "Nonnegative matrix factorization (NMF) is widely used for clustering with strong interpretability. Among general NMF problems, symmetric NMF is a special one that plays an important role in graph clustering where each element measures the similarity between data points. Most existing symmetric NMF algorithms require factor matrices to be nonnegative, and only focus on minimizing the gap between similarity matrix and its approximation for clustering, without giving a consideration to other potential regularization terms which can yield better clustering. In this paper, we explore factorizing a symmetric matrix that does not have to be nonnegative, presenting an efficient factorization algorithm with a regularization term to boost the clustering performance. Moreover, a more general framework is proposed to solve symmetric matrix factorization problems with different constraints on the factor matrices." @default.
- W4320060390 created "2023-02-12" @default.
- W4320060390 creator A5051363890 @default.
- W4320060390 creator A5081085876 @default.
- W4320060390 date "2022-11-01" @default.
- W4320060390 modified "2023-10-18" @default.
- W4320060390 title "Rethinking Symmetric Matrix Factorization: A More General and Better Clustering Perspective" @default.
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- W4320060390 doi "https://doi.org/10.1109/icdm54844.2022.00080" @default.
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