One-step multi-view clustering via deep-level semantics exploiting

Abstract

Multi-view clustering (MVC) has gained promising performance improvement compared with traditional signal-view clustering due to the complementary information of multiple views. However, existing MVC methods exploit clustering structure by utilising signal-layer mapping, such that they cannot exploit the underlying deep-level semantic information in complex and interleaved multi-view data. Moreover, existing methods usually conduct multi-view fusion and clustering separately, which results in unpromising performance. To address the above problems, one-step MVC via deep-level semantics exploiting (DLSE) is proposed to exploit deep-level semantic information and learn the indicator matrix using a one-step manner. To be specific, a novel deep matrix factorisation (DMF) paradigm is designed to exploit the hierarchical semantics via a layer-wise scheme, so that samples from the same clusters are forced to be closer in the low-dimensional space layer by layer. Furthermore, to make the learned representation preserve the local geometric structure of data, DLSE introduces a local preservation regularisation to guide DMF. Meanwhile, by employing spectral rotating fusion, the cluster indicator can be obtained directly. Extensive experiments demonstrate the superiority of DLSE in contrast with some state-of-the-art methods.