Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent

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. We dig out as well as deploy the dependency amongst views through hierarchical feature descent, which leads to a common latent space( STAGE 1). This latent space, for the first time of its kind, is regarded as a 'resemblance space', as it reveals certain correlations and dependencies of different views. To be exact, the one-hot encoding of a category can also be referred to as a resemblance space in its terminal phase. Moreover, due to the intrinsic fact that most of the existing multi-view clustering algorithms stem from k-means clustering and spectral clustering, this results in cubic time complexity w.r.t. the number of the objects. However, we propose Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent(MVSC-HFD) to further reduce the computing complexity to linear time cost through a unified sampling strategy in resemblance space( STAGE 2), followed by subspace clustering to learn the representation collectively( STAGE 3). Extensive experimental results on public benchmark datasets demonstrate that our proposed model consistently outperforms the state-of-the-art techniques.