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Fast Multiview Clustering With Spectral Embedding
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DOI：10.1109/TIP.2022.3176223.png)
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摘要
En 中文
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.
Keyword:
Spectral analysis
Optimization
Complexity theory
Clustering methods
Clustering algorithms
Linear programming
Task analysis
Multi-view clustering
spectral embedding
anchor graph
orthogonality
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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Multi-view subspace clustering via simultaneously learning the representation tensor and affinity matrix.png)
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