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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Vydané v:IEEE transactions on knowledge and data engineering Ročník 34; číslo 7; s. 3349 - 3362
Hlavní autori: Luong, Khanh, Nayak, Richi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Shrnutí: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 representation of the multi-dimensional data, has shown effectiveness. We propose to include the inter-manifold in the NMF framework, utilizing the distance information of data points of different data types (or views) to learn the diverse manifold for data clustering. Empirical analysis reveals that the proposed method can find partial representations of various interrelated types and select useful features during clustering. Results on several datasets demonstrate that the proposed method outperforms the state-of-the-art multi-aspect data clustering methods in both accuracy and efficiency.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2020.3022072