Multi-view Subspace Clustering via Partition Fusion
This work addresses noise and inconsistency issues in multi-view clustering, which is important for unsupervised data analysis, but it appears incremental as it builds on existing subspace clustering methods.
The paper tackles the problem of performance degradation in multi-view subspace clustering due to noise and feature inconsistency by fusing multi-view information in a partition space, resulting in enhanced robustness as verified by experiments on benchmark datasets.
Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way. Among various methods, the multi-view subspace clustering approach has gained increasing attention due to its encouraging performance. Basically, it integrates multi-view information into graphs, which are then fed into spectral clustering algorithm for final result. However, its performance may degrade due to noises existing in each individual view or inconsistency between heterogeneous features. Orthogonal to current work, we propose to fuse multi-view information in a partition space, which enhances the robustness of Multi-view clustering. Specifically, we generate multiple partitions and integrate them to find the shared partition. The proposed model unifies graph learning, generation of basic partitions, and view weight learning. These three components co-evolve towards better quality outputs. We have conducted comprehensive experiments on benchmark datasets and our empirical results verify the effectiveness and robustness of our approach.