A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …

Hyperspectral imagery contains hundreds of contiguous bands with a wealth of spectral
signatures, making it possible to distinguish materials through subtle spectral discrepancies …

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 …

As one category of important incomplete multi-view clustering methods, subspace based
methods seek the common latent representation of incomplete multi-view data by matrix …

In this paper we introduce a novel supervised manifold learning technique called
Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to …

Feature selection has become one of the hot research topics in the era of big data. At the
same time, as an extension of single-valued data, interval-valued data with its inherent …

Existing methods for multi-view gait-based identification mainly focus on transforming the
features of one view to the features of another view, which is technically sound but has …

Graph-based technique is widely used in projection, clustering, and classification tasks. In
this article, we propose a novel and solid framework, named unsupervised projection with …

Perceptual image hashing is an effective and efficient way to identify images in large-scale
databases, where two major performances are robustness and discrimination. A better …

As we aim at alleviating the curse of high-dimensionality, subspace learning is becoming
more popular. Existing approaches use either information about global or local structure of …