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• W3158578045 abstract "Non-negative matrix factorization (NMF) is a powerful tool for dimensionality reduction and clustering. Unfortunately, the interpretation of the clustering results from NMF is difficult, especially for the high-dimensional biological data without effective feature selection. In this paper, we first introduce a row-sparse NMF with $ell_{2,0}$-norm constraint (NMF_$ell_{20}$), where the basis matrix $W$ is constrained by the $ell_{2,0}$-norm, such that $W$ has a row-sparsity pattern with feature selection. It is a challenge to solve the model, because the $ell_{2,0}$-norm is non-convex and non-smooth. Fortunately, we prove that the $ell_{2,0}$-norm satisfies the Kurdyka-L{ojasiewicz} property. Based on the finding, we present a proximal alternating linearized minimization algorithm and its monotone accelerated version to solve the NMF_$ell_{20}$ model. In addition, we also present a orthogonal NMF with $ell_{2,0}$-norm constraint (ONMF_$ell_{20}$) to enhance the clustering performance by using a non-negative orthogonal constraint. We propose an efficient algorithm to solve ONMF_$ell_{20}$ by transforming it into a series of constrained and penalized matrix factorization problems. The results on numerical and scRNA-seq datasets demonstrate the efficiency of our methods in comparison with existing methods." @default.
• W3158578045 created "2021-05-10" @default.
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• W3158578045 date "2021-04-27" @default.
• W3158578045 modified "2023-09-23" @default.
• W3158578045 title "Structured Sparse Non-negative Matrix Factorization with L20-Norm for scRNA-seq Data Analysis." @default.
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