Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis

By Hyunsoo Kim, Haesun Park. Bioinform., 2007

912 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0e6a0c9cd9c492da43a78ac9552fc3f82b6c1da8

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Citations in the corpus

The Why and How of Nonnegative Matrix Factorization (2014, methodology): “…(ANLS) is a class of methods where the subproblems in W and H are solved exactly, that is, the update for W is given by Many methods can be used to solve the NNLS argmin W ≥ 0 || X − W H || F , and dedicated active-set methods have shown to perform very well in practice 6 ; see [72, 73, 75].”
Compressive light-field microscopy for 3D neural activity recording (2016, methodology): “Our implementation uses an active-set approach to alternating non-negative least squares in order to find a locally optimal solution in polynomial time [26].”
Sign-constrained least squares estimation for high-dimensional regression (2012, methodology): “, 2010) and non-negative least squares regression can be a useful tool for this factorization (Kim and Park, 2007).”
RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach (2016, methodology): “In addition, iDeep also performes better than other matrix factorization-based methods NMF [30], SNMF [31] and QNO [32], which achieves the average AUC of 0.83 ± 0.10, 0.71 ± 0.14, 0.79 ± 0.12 on 31 experiments, respectively.”
Nonnegative Matrix Factorization: A Comprehensive Review (2013, methodology): “One of the highlights in NSC is to employ active set based algorithm to solve the least-squares minimization under the nonnegativity and sparseness constraints, i.e., modified Least Angle Regression and Selection (LARS) [89] and NLS [26].”
Guilt by association: large scale malware detection by mining file-relation graphs (2014, methodology): “To test this, we turn to entropy, a widely used measure for determining the uncertainty or irregularity of a system [19].”
Generalized Low Rank Models (2014, methodology): “However, these problems have a rich analytical structure which can sometimes be exploited [49, 10, 31], and a wide range of uses in practice [85, 126, 7, 151, 77, 47].”
Sparse Nonnegative Matrix Factorization for Clustering (2008, methodology): “Brunet et al. [4] and Kim and Park [ 13 ] successfully used this method to determine the unknown number of groups from gene expression data.”
A flexible R package for nonnegative matrix factorization (2010, methodology): “The NMF package implements a scoring and selection method proposed in [23] to extract the most metagene-specific genes.”
On the Complexity of Nonnegative Matrix Factorization (2007, background): “Sparsity may be imposed as a term in the objective function [8].”
Fast and Efficient Estimation of Individual Ancestry Coefficients (2014, methodology): “NMF was previously applied to gene expression data (Kim and Park 2007), and algorithms for NMF were surveyed and compared in (Kim and Park 2011).”
Symmetric Nonnegative Matrix Factorization for Graph Clustering (2012, background): “NMF has received wide attention in clustering with many types of data, including documents [22], images [3], and microarray data [10].”
Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method (2008, methodology): “Applications of the NMF are numerous including image processing [21], text data mining [31], subsystem identification [19], cancer class discovery [4] [8] [18], etc.”