Multiview Clustering: A Scalable and Parameter-Free Bipartite Graph Fusion Method

Multiview clustering partitions data into different groups according to their heterogeneous features. Most existing methods degenerate the applicability of models due to their intractable hyper-parameters triggered by various regularization terms. Moreover, traditional spectral based methods always...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 44; číslo 1; s. 330 - 344
Hlavní autori:	Li, Xuelong, Zhang, Han, Wang, Rong, Nie, Feiping
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Bipartite graph Clustering Clustering algorithms Computer science connectivity constraint Data models Electronic mail graph fusion Graph theory Graphs initialization-independent Laplace equations Mathematical models Multiview clustering Optical imaging Parameters Regularization scalable and parameter-free
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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