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.

中文翻译：

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通过深层语义利用的一步多视图聚类

与传统的信号视图聚类相比，多视图聚类（MVC）由于多视图的互补信息而获得了可喜的性能改进。然而，现有的MVC方法通过利用信号层映射来利用聚类结构，使得它们无法利用复杂且交错的多视图数据中的底层深层语义信息。此外，现有方法通常分别进行多视图融合和聚类，这导致性能不佳。为了解决上述问题，提出了通过深层语义利用的一步MVC（DLSE）来利用深层语义信息并使用一步方式学习指示矩阵。具体来说，一种新颖的深度矩阵分解（DMF）范式旨在通过逐层方案利用分层语义，从而迫使来自同一簇的样本在低维空间中逐层靠近。此外，为了使学习到的表示保留数据的局部几何结构，DLSE引入了局部保留正则化来指导DMF。同时，通过光谱旋转融合，可以直接获得聚类指标。大量的实验证明了 DLSE 与一些最先进的方法相比的优越性。