In this paper, we study two challenging problems in incomplete multi-view clustering
analysis, namely, i) how to learn an informative and consistent representation among …

In this article, we propose a unified framework to solve the following two challenging
problems in incomplete multi-view representation learning: i) how to learn a consistent …

Graph-based subspace clustering methods have exhibited promising performance.
However, they still suffer some of these drawbacks: they encounter the expensive time …

Multiview learning (MVL), which enhances the learners' performance by coordinating
complementarity and consistency among different views, has attracted much attention. The …

Virtual simulated scenes are becoming a critical part of autonomous driving. In the context of
knowledge automation and machine learning, simulated images are widely used for visual …

Clustering aims to partition an input dataset into distinct groups according to some distance
or similarity measurements. One of the most widely used clustering method nowadays is the …

Multi-modal clustering (MMC) aims to explore complementary information from diverse
modalities for clustering performance facilitating. This article studies challenging problems in …

Taking the assumption that data samples are able to be reconstructed with the dictionary
formed by themselves, recent multiview subspace clustering (MSC) algorithms aim to find a …

Multi-view subspace clustering algorithms have recently been developed to process multi-
view dataset clustering by accurately depicting the essential characteristics of multi-view …

Clustering large and high-dimensional document data has got a great interest. However,
current clustering algorithms lack efficient representation learning. Implementing deep …