In this paper, we extend the original non-negative matrix factoriza- tion (NMF) to kernel NMF (KNMF). The advantages of KNMF over NMF are: 1) it could extract more useful features hidden in the original data th
Li, T. Zhao, Projected gradient method for kernel discriminant nonnegative matrix factorization and the applications, Signal Processing 90 (7) (2010) 2150-2163.Liang, Zhizheng ; Li, Youfu ; Zhao, Tuo: Projected gradient method for kernel dis- criminant nonnegative matrix factorization and the ...
Nonnegative matrix factorization (NMF) is widely used to analyze high-dimensional count data because, in contrast to real-valued alternatives such as factor analysis, it produces an interpretable parts-based representation. However, in applications such as spatial transcriptomics, NMF fails to incorporat...
The Why and How of Nonnegative Matrix Factorization 2014, Regularization, Optimization, Kernels, and Support Vector Machines Nonnegative matrix factorization: A comprehensive review 2013, IEEE Transactions on Knowledge and Data Engineering Symmetric nonnegative matrix factorization for graph clustering 2012,...
Non-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix
In the last few years, the Non-negative Matrix Factorization ( NMF ) technique has gained a great interest among the Bioinformatics community, since it is able to extract interpretable parts from high-dimensional datasets. However, the computing time req
In the last few years, the Non-negative Matrix Factorization ( NMF ) technique has gained a great interest among the Bioinformatics community, since it is able to extract interpretable parts from high-dimensional datasets. However, the computing time req
Gillis, N.: The why and how of nonnegative matrix factorization. In: Suykens, J., Signoretto, M., Argyriou, A. (eds.) Regularization, Optimization, Kernels, and Support Vector Machines, Machine Learning and Pattern Recognition chap 12, pp. 257–291. Chapman & Hall/CRC, Boca Raton, Fl...
unlike the traditional nonnegative matrix factorization (NMF) methods, we addedL2, 1-norm as well as GIP (Gaussian interaction profile) kernels into the NMF model. TheL2, 1-norm was added to increase the disease matrix sparsity and eliminate unattached disease pairs [51,52,53]. Moreover, Ti...
Intersecting Faces: Non-negative Matrix Factorization With New Guarantees (2015) The why and how of nonnegative matrix factorization (2014) Computing a Nonnegative Matrix Factorization – Provably (2011) Problems with Provable Global Results Matrix Completion/Sensing (See also low-rank matrix/tensor re...