In this paper, we propose novel strategies for neutral vector variable decorrelation. Two
fundamental invertible transformations, namely, serial nonlinear transformation and parallel …

Independent component analysis (ICA) is a statistical method for transforming an observed
multidimensional random vector into components that are statistically as independent from …

Accurately evaluating statistical independence among random variables is a key element of
independent component analysis (ICA). In this letter, we employ a squared-loss variant of …

A fundamental problem in neural network research, as well as in many other disciplines, is
finding a suitable representation of multivariate data, ie random vectors. For reasons of …

We propose a robust approach for independent component analysis (ICA) of signals where
observations are contaminated with high-level additive noise and/or outliers. The source …

The author (1997) introduced a large family of one-unit contrast functions to be used in
independent component analysis (ICA). In this paper, the family is analyzed mathematically …

Constrained independent component analysis (cICA) is a general framework to incorporate
a priori information from problem into the negentropy contrast function as constrained terms …

A novel neural network technique for nonnegative independent component analysis is
proposed in this letter. Compared with other algorithms, this method can work efficiently …

In this paper, we propose a novel test of independence based on the concept of correntropy.
We explore correntropy from a statistical perspective and discuss its properties in the context …

In this paper, we introduce a novel independent component analysis (ICA) algorithm, which
is truly blind to the particular underlying distribution of the mixed signals. Using a …