Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent
This addresses computational bottlenecks in multi-view clustering for applications like public affairs, though it appears incremental as it builds on existing anchor-based and subspace clustering approaches.
The paper tackles the problems of feature alignment across views and high computational complexity in multi-view clustering by proposing MVSC-HFD, which uses hierarchical feature descent to align views and reduces time complexity from cubic to linear while achieving state-of-the-art performance on benchmark datasets.
Multi-view clustering has attracted growing attention owing to its capabilities of aggregating information from various sources and its promising horizons in public affairs. Up till now, many advanced approaches have been proposed in recent literature. However, there are several ongoing difficulties to be tackled. One common dilemma occurs while attempting to align the features of different views. {Moreover, due to the fact that many existing multi-view clustering algorithms stem from spectral clustering, this results to cubic time complexity w.r.t. the number of dataset. However, we propose Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent(MVSC-HFD) to tackle the discrepancy among views through hierarchical feature descent and project to a common subspace( STAGE 1), which reveals dependency of different views. We further reduce the computational complexity to linear time cost through a unified sampling strategy in the common subspace( STAGE 2), followed by anchor-based subspace clustering to learn the bipartite graph collectively( STAGE 3). }Extensive experimental results on public benchmark datasets demonstrate that our proposed model consistently outperforms the state-of-the-art techniques.