Abstract The Hilbert–Schmidt independence criterion (HSIC) was originally designed to
measure the statistical dependence of the distribution-based Hilbert space embedding in …

Feature selection is employed to reduce the feature dimensions and computational
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …

Non-negative matrix factorization (NMF) has attracted sustaining attention in multi-view
clustering, because of its ability of processing high-dimensional data. In order to learn the …

During the past several years, as one of the most successful applications of sparse coding
and dictionary learning, dictionary-based face recognition has received significant attention …

This book revolves around the question of designing a matrix D∈ Rm× n called dictionary,
such that to obtain good sparse representations y≈ Dx for a class of signals y∈ Rm given …

A realistic and comprehensive review of joint approaches to machine learning and signal
processing algorithms, with application to communications, multimedia, and biomedical …

Dictionary learning, which is based on sparse coding, has been frequently applied to many
tasks related to remote sensing processes. Recently, many new non-analytic dictionary …

In this paper, we present a new approach of incorporating kernels into dictionary learning.
The kernel K-SVD algorithm (KKSVD), which has been introduced recently, shows an …

In this paper, we propose a semisupervised label consistent dictionary learning (SSDL)
framework for machine fault classification. SSDL is a semisupervised extension of recent …

Magnetotelluric is one of the mainstream exploration geophysical methods, which plays a
vital role in studying deep geological structures and finding deep hidden blind ore bodies …