Abstract On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering
Current nonnegative matrix factorization (NMF) deals with X = FG T type. We provide a systematic analysis and extensions of NMF to the symmetric W = HH T, and the weighted W = HSH T.
Xiaofeng He, Chris Ding, Horst D. Simon
core
Large-scale semidefinite programming with graphics processing units. [PDF]
Han Q +6 more
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From Common Information to Canonical Correlation Analysis and Beyond: Bridging Information Theory, Signal Processing, and Machine Learning. [PDF]
Karakasis PA, Saad W.
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Discovering interpretable low-dimensional dynamics using maximum entropy. [PDF]
Chung MC, Mohan T, Dixit PD, Guan J.
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Neighborhood nonnegative matrix factorization identifies patterns and spatially-variable genes in large-scale spatial transcriptomics data. [PDF]
Laursen R +4 more
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Nonnegative matrix factorizationalgorithms and applications
Data-mining has become a hot topic in recent years. It consists of extracting relevant information or structures from data such as: pictures, textual material, networks, etc.
Ngoc-Diep, Ho
core
Probing the content of semantic representations in body-selective regions. [PDF]
Yashiro R +3 more
europepmc +1 more source
A multilevel approach for nonnegative matrix factorization [PDF]
Nonnegative Matrix Factorization (NMF) is the problem of approximating a nonnegative matrix with the product of two low-rank nonnegative matrices and has been shown to be particularly useful in many applications, e.g., in text mining, image processing ...
GILLIS, Nicolas, GLINEUR, François
core
Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data. [PDF]
Li Z +10 more
europepmc +2 more sources
GrSrNMF: dynamic community detection with graph and symmetry bi-regularized non-negative matrix factorization. [PDF]
Yu W +6 more
europepmc +1 more source

