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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Vydáno v:IEEE transactions on pattern analysis and machine intelligence Ročník 44; číslo 1; s. 330 - 344
Hlavní autoři: Li, Xuelong, Zhang, Han, Wang, Rong, Nie, Feiping
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Shrnutí: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 encounter the expensive time overheads and fail in exploring the explicit clusters from graphs. In this paper, we present a scalable and parameter-free graph fusion framework for multiview clustering, seeking for a joint graph compatible across multiple views in a self-supervised weighting manner. Our formulation coalesces multiple view-wise graphs straightforward and learns the weights as well as the joint graph interactively, which could actively release the model from any weight-related hyper-parameters. Meanwhile, we manipulate the joint graph by a connectivity constraint such that the connected components indicate clusters directly. The designed algorithm is initialization-independent and time-economical which obtains the stable performance and scales well with the data size. Substantial experiments on toy data as well as real datasets are conducted that verify the superiority of the proposed method compared to the state-of-the-arts over the clustering performance and time expenditure.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2020.3011148