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|a 10.1109/TIP.2022.3176223
|2 doi
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|a eng
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|a Yang, Ben
|e verfasserin
|4 aut
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| 245 |
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|a Fast Multiview Clustering With Spectral Embedding
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 10.06.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Spectral clustering has been a hot topic in unsupervised learning owing to its remarkable clustering effectiveness and well-defined framework. Despite this, due to its high computation complexity, it is unable of handling large-scale or high-dimensional data, particularly multi-view large-scale data. To address this issue, in this paper, we propose a fast multi-view clustering algorithm with spectral embedding (FMCSE), which speeds up both the spectral embedding and spectral analysis stages of multi-view spectral clustering. Furthermore, unlike conventional spectral clustering, FMCSE can acquire all sample categories directly after optimization without extra k-means, which can significantly enhance efficiency. Moreover, we also provide a fast optimization strategy for solving the FMCSE model, which divides the optimization problem into three decoupled small-scale sub-problems that can be solved in a few iteration steps. Finally, extensive experiments on a variety of real-world datasets (including large-scale and high-dimensional datasets) show that, when compared to other state-of-the-art fast multi-view clustering baselines, FMCSE can maintain comparable or even better clustering effectiveness while significantly improving clustering efficiency
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|a Journal Article
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| 700 |
1 |
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|a Zhang, Xuetao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Nie, Feiping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Fei
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 31(2022) vom: 24., Seite 3884-3895
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|x 1941-0042
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|g volume:31
|g year:2022
|g day:24
|g pages:3884-3895
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|u http://dx.doi.org/10.1109/TIP.2022.3176223
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