  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.
