Computer Science > Computer Vision and Pattern Recognition
[Submitted on 3 Jan 2024 (v1), last revised 28 May 2024 (this version, v3)]
Title:One-Step Late Fusion Multi-view Clustering with Compressed Subspace
View PDF HTML (experimental)Abstract:Late fusion multi-view clustering (LFMVC) has become a rapidly growing class of methods in the multi-view clustering (MVC) field, owing to its excellent computational speed and clustering performance. One bottleneck faced by existing late fusion methods is that they are usually aligned to the average kernel function, which makes the clustering performance highly dependent on the quality of datasets. Another problem is that they require subsequent k-means clustering after obtaining the consensus partition matrix to get the final discrete labels, and the resulting separation of the label learning and cluster structure optimization processes limits the integrity of these models. To address the above issues, we propose an integrated framework named One-Step Late Fusion Multi-view Clustering with Compressed Subspace (OS-LFMVC-CS). Specifically, we use the consensus subspace to align the partition matrix while optimizing the partition fusion, and utilize the fused partition matrix to guide the learning of discrete labels. A six-step iterative optimization approach with verified convergence is proposed. Sufficient experiments on multiple datasets validate the effectiveness and efficiency of our proposed method.
Submission history
From: Qiyuan Ou [view email][v1] Wed, 3 Jan 2024 06:18:30 UTC (135 KB)
[v2] Tue, 9 Apr 2024 13:59:18 UTC (177 KB)
[v3] Tue, 28 May 2024 08:58:59 UTC (177 KB)
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