Deep multi-view clustering (MVC) is to mine and employ the complex relationships among
views to learn the compact data clusters with deep neural networks in an unsupervised …

Multi-view clustering has attracted increasing attention by reason of its ability to leverage the
complementarity of multi-view data. Existing multi-view clustering methods have explored …

To improve the accuracy of color image completion with missing entries, we present a
recovery method based on generalized higher-order scalars. We extend the traditional …

The development of skin cancer can be influenced by the abnormal expression of certain
microRNAs (miRNAs). Current prediction models for miRNA-skin cancer associations have …

Feature selection is an effective way to improve the model learning performance while being
a challenging problem in the Partial Multi-label Learning (PML). Different from multi-label …

Multi-view clustering has garnered considerable attention in recent years owing to its
impressive performance in processing high-dimensional data. Most multi-view clustering …

Incomplete multi-view clustering aims to assign data samples into cohesive groups with
partially available information from multiple views. In this paper, we propose a novel …

Large-scale subspace clustering usually drops the requirements of the full similarity matrix
and Laplacian matrix but constructs the anchor affinity matrix and uses matrix approximation …

Sparse subspace clustering (SSC) focuses on revealing data distribution from algebraic
perspectives and has been widely applied to high-dimensional data. The key to SSC is to …

In deep multi-view clustering, three intractable problems are posed ahead of researchers,
namely, the complementarity exploration problem, the information preservation problem …