Abstract:
Multimedia, machine learning, and data mining communities are focusing on multi-view clustering. Multi-view subspace clustering (MVSC) is a popular multi-view clustering algorithm because it can reveal the intrinsic low-dimensional clustering structure hidden across views.
Despite superior clustering performance in various applications, existing MVSC methods directly fuse multi-view information in the similarity level by merging noisy affinity matrices and isolate affinity learning, multi-view information fusion, and clustering. Both factors may reduce multi-view information use, resulting in poor clustering performance.
This paper proposes a new consensus one-step multi-view subspace clustering (COMVSC) method to address these issues. COMVSC optimally integrates discriminative partition-level information instead of directly fusing affinity matrices to reduce data noise. Additionally, the affinity matrices, consensus representation, and final clustering labels matrix are learned together.
Thus, the three steps can negotiate to optimize clustering performance. Thus, we propose an iterative optimization algorithm. Our method outperforms state-of-the-art approaches in benchmark dataset experiments.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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