Minimizing the discrepancy of feature distributions between different domains is one of the
most promising directions in unsupervised domain adaptation. From the perspective of …

With solid theoretical foundations and numerous potential applications, Blind Signal
Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume …

Unsupervised domain adaptation aims to annotate unlabeled target domain samples by
utilizing transferable knowledge learned from the source domain. Optimal transport (OT) has …

Under-determined mixtures are characterized by the fact that they have more inputs than
outputs, or, with the antenna array processing terminology, more sources than sensors. The …

The last fifteen years have witnessed a tremendous resurgence in research and applications
in the area of higher-order statistics (HOS), a broad term encompassing statistical …

How to measure the domain discrepancy is of significant importance in the field of
unsupervised domain adaptation. Among them, Correlation Alignment (CORAL), aligning …

Abstract Independent Component Analysis (ICA) represents a higher-order statistical
technique that is often used to separate mixtures of stochastic random signals into …

A myriad of applications require the extraction of a set of signals which are not directly
accessible. Instead, this extraction must be carried out from another set of measurements …

This paper presents a new approach to recover original signals (“sources”) from their linear
mixtures, observed by the same number of sensors. The algorithms proposed only assume …

Contrast-based separation of sources have a number of advantages. Among others, they
are optimal (in a precise sense) in the presence of noise of unknown statistics. Here a new …