Learning Inter- and Intra-Manifolds for Matrix Factorization-Based Multi-Aspect Data Clustering

Clustering on the data with multiple aspects, such as multi-view or multi-type relational data, has become popular in recent years due to their wide applicability. The approach using manifold learning with the Non-negative Matrix Factorization (NMF) framework, that learns the accurate low-rank repre...

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Vydáno v:	IEEE transactions on knowledge and data engineering Ročník 34; číslo 7; s. 3349 - 3362
Hlavní autoři:	Luong, Khanh, Nayak, Richi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.07.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Clustering Clustering methods Data points Empirical analysis Factorization Hypertext systems laplacian regularization Learning systems Machine learning manifold learning Manifolds Manifolds (mathematics) Matrix decomposition Multi-type relational data/ clustering multi-view data/ clustering Multidimensional data nearest neighbours non-negative matrix factorization Representations Sparse matrices Symmetric matrices
ISSN:	1041-4347, 1558-2191
On-line přístup:	Získat plný text
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