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Multi-view clustering has attracted growing attention owing to itscapabilities of aggregating information from various sources and its promisinghorizons in public affairs. Up till now, many advanced approaches have beenproposed in recent literature. However, there are several ongoing difficultiesto be tackled. One common dilemma occurs while attempting to align the featuresof different views. We dig out as well as deploy the dependency amongst viewsthrough 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 ofdifferent views. To be exact, the one-hot encoding of a category can also bereferred to as a resemblance space in its terminal phase. Moreover, due to theintrinsic fact that most of the existing multi-view clustering algorithms stemfrom k-means clustering and spectral clustering, this results in cubic timecomplexity w.r.t. the number of the objects. However, we propose Anchor-basedMulti-view Subspace Clustering with Hierarchical Feature Descent(MVSC-HFD) tofurther reduce the computing complexity to linear time cost through a unifiedsampling strategy in resemblance space( STAGE 2), followed by subspaceclustering to learn the representation collectively( STAGE 3). Extensiveexperimental results on public benchmark datasets demonstrate that our proposedmodel consistently outperforms the state-of-the-art techniques.

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