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 …

We present a Bayesian approach for sparse component analysis (SCA) in the noisy case.
The algorithm is essentially a method for obtaining sufficiently sparse solutions of …

A two-stage clustering-then-ℓ1-optimization approach has been often used for sparse
component analysis (SCA). The first challenging task of this approach is to estimate the …

In the last decade, kernel methods have become established techniques to perform
nonlinear signal processing. Thanks to their foundation in the solid mathematical framework …

We consider the problem of blind sparse deconvolution, which is common in both image and
signal processing. To counter-balance the ill-posedness of the problem, many approaches …

This paper studies the existing links between two approaches of Independent Component
Analysis (ICA)–FastICA/projection pursuit and Infomax/Maximum likelihood estimation–and …

Background As a blind source separation technique, independent component analysis (ICA)
has many applications in functional magnetic resonance imaging (fMRI). Although either …

Blind source separation is a basic topic in signal and image processing. Independent
component analysis is a basic solution to blind source separation. This chapter introduces …

Independent component analysis (ICA) is widely used to estimate spatial resting-state
networks and their time courses in neuroimaging studies. It is thought that independent …

With the increase in computerisation in every field, a huge amount of data is collected from
everywhere. Therefore, extracting useful information has become a necessary task in the …