Sparse Nonnegative Matrix Factorization for Clustering

By Jingu Kim, Haesun Park. 2008

247 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

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Citations in the corpus

Efficient Content-Based Sparse Attention with Routing Transformers (2020, methodology): “This strategy draws inspiration from the application of k-means clustering to Non-negative Matrix Factorization (NMF) (Lee and Seung, 2001; Ding et al., 2005; Kim and Park, 2008), which is relevant to the sparsification of non-negative matrices like”
Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework (2013, methodology): “In the data mining community, variants of NMF such as 988 convex and semi-NMFs [25,75], orthogonal tri-NMF [26], and group-sparse NMF [56] have 989 been proposed, and using NMF for clustering has been shown to be successful [12,57,63].”
Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L2, 1 Regularization (2015, background): “[19] introduced sparsity constraints on the latent feature matrix, which will give more sparse latent representations.”
Symmetric Nonnegative Matrix Factorization for Graph Clustering (2012, background): “It is interesting to note that most success of NMF in clustering has been around document clustering [22, 14, 12].”
Phase Retrieval Using Alternating Minimization (2013, background): “Theorem 4.1 shows that when A is sampled from standard complex normal distribution, this initialization is accurate.”
Estimation of Simultaneously Sparse and Low Rank Matrices (2012, methodology): “There is no strong guarantee on the sparsity achieved by NMF nor is it easy to set the target sparsity and different methods for sparse NMF have been proposed in (Hoyer, 2004; Kim & Park, 2008).”
Fast Nonnegative Matrix Factorization: An Active-Set-Like Method and Comparisons (2011, methodology): “For example, when sparsity is imposed on factor H , the resulting NMF can be used as a clustering algorithm [16, 18].”
Low-rank matrix completion using alternating minimization (2012, methodology): “For example, clustering [16], sparse PCA [25], non-negative matrix factorization [15], signed network prediction [9] etc.”
A Semi-NMF-PCA Unified Framework for Data Clustering (2017, methodology): “The analysis of NMF versus k -means has been recently discussed by Kim and Park [22], who proposed Sparse NMF (SNMF) for data clustering.”