Ensemble learning techniques have achieved state-of-the-art performance in diverse
machine learning applications by combining the predictions from two or more base models …

Despite significant progress, there remain three limitations to the previous multi-view
clustering algorithms. First, they often suffer from high computational complexity, restricting …

Aligning distributions of view representations is a core component of today's state of the art
models for deep multi-view clustering. However, we identify several drawbacks with naively …

Multiple kernel clustering (MKC) optimally utilizes a group of pre-specified base kernels to
improve clustering performance. Among existing MKC algorithms, the recently proposed late …

Nowadays, with the rapid development of data collection sources and feature extraction
methods, multi-view data are getting easy to obtain and have received increasing research …

The existing clustering validity indexes (CVIs) show some difficulties to produce the correct
cluster number when some cluster centers are close to each other, and the separation …

Multi-view clustering has attracted increasing attention in recent years by exploiting common
clustering structure across multiple views. Most existing multi-view clustering algorithms use …

Multi-modal clustering aims to cluster data into different groups by exploring complementary
information from multiple modalities or views. Little work learns the deep fused …

In recent years, numerous multi-view subspace clustering methods have been proposed to
exploit the complementary information from multiple views. Most of them perform data …

The existing deep multiview clustering (MVC) methods are mainly based on autoencoder
networks, which seek common latent variables to reconstruct the original input of each view …