Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

In many machine learning applications, data are coming with multiple graphs, which is
known as the multiple graph learning problem. The problem of multiple graph learning is to …

Multiview clustering partitions data into different groups according to their heterogeneous
features. Most existing methods degenerate the applicability of models due to their …

Although previous graph-based multi-view clustering (MVC) algorithms have gained
significant progress, most of them are still faced with three limitations. First, they often suffer …

In real-world applications, it is often that the collected multi-view data are incomplete, ie,
some views of samples are absent. Existing clustering methods for incomplete multi-view …

Graph-based clustering methods perform clustering on a fixed input data graph. If this initial
construction is of low quality then the resulting clustering may also be of low quality …

Learning graphs from data automatically have shown encouraging performance on
clustering and semisupervised learning tasks. However, real data are often corrupted, which …

Graphs have become increasingly popular in modeling structures and interactions in a wide
variety of problems during the last decade. Graph-based clustering and semi-supervised …

Unsupervised feature selection chooses a subset of discriminative features to reduce feature
dimension under the unsupervised learning paradigm. Although lots of efforts have been …

Traditional graph clustering methods consist of two sequential steps, ie, constructing an
affinity matrix from the original data and then performing spectral clustering on the resulting …