Feature extraction and dimensionality reduction are important tasks in many fields of science
dealing with signal processing and analysis. The relevance of these techniques is …

Pattern Analysis is the process of finding general relations in a set of data, and forms the
core of many disciplines, from neural networks, to so-called syntactical pattern recognition …

Objective: In the steady-state visual evoked potential (SSVEP)-based brain computer
interfaces (BCIs), spatial filtering, which combines the multi-channel …

This paper presents a comprehensive review of the historical development, the current state
of the art, and prospects of data-driven approaches for industrial process monitoring. The …

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

Canonical correlation analysis (CCA) is a well-known data analysis technique that extracts
multidimensional correlation structure between two sets of variables. CCA focuses on …

The task of studying the properties of configurations of points embedded in a metric space
has long been a central task in pattern recognition, but has acquired even greater …

With the increasing availability of annotated multimedia data on the Internet, techniques are
in demand that allow for a principled joint processing of different types of data. Multiview …

Similar to other data mining and machine learning tasks, multi-label learning suffers from
dimensionality. An effective way to mitigate this problem is through dimensionality reduction …

This paper proposes a canonical variate analysis (CVA) approach based on feature
representation of canonical correlation for the monitoring of faults associated with changes …