In recent years, unsupervised feature selection methods have raised considerable interest in
many research areas; this is mainly due to their ability to identify and select relevant features …

Community detection is one of the popular research topics in the field of complex networks
analysis. It aims to identify communities, represented as cohesive subgroups or clusters …

Multi-view clustering (MVC) has received extensive attention due to its efficient processing of
high-dimensional data. Most of the existing multi-view clustering methods are based on non …

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 …

Nowadays, multi-view clustering has attracted more and more attention. To date, almost all
the previous studies assume that views are complete. However, in reality, it is often the case …

Providing a broad but in-depth introduction to neural network and machine learning in a
statistical framework, this book provides a single, comprehensive resource for study and …

Real data are often collected from multiple channels or comprised of different
representations (ie, views). Multi-view learning provides an elegant way to analyze the multi …

In this paper, a new unsupervised learning algorithm, namely Nonnegative Discriminative
Feature Selection (NDFS), is proposed. To exploit the discriminative information in …

With the advance of technology, data are often with multiple modalities or coming from
multiple sources. Multi-view clustering provides a natural way for generating clusters from …

Dimensionality reduction has attracted increasing attention, because high-dimensional data
have arisen naturally in numerous domains in recent years. As one popular dimensionality …