Pervasive electroencephalographic (EEG) artifacts associated with blinks, eye-movements, muscle noise, cardiac signals , and line noise poses a major challenge for EEG interpretation and analysis. Here, we propose a generally applicable method for removing a wide variety of artifacts from EEG records based on an extended version of an Independent Component Analysis (ICA) algorithm 2, 12] for pe...

In this paper, a new feature extraction algorithm considering both two-directional two-dimensional principal component analysis ((2D)2PCA) and independent component analysis(ICA), called (2D)2PCA-ICA, is proposed for face representation. This algorithm analyzes the principal components of image vectors on 2D matrices by simultaneously considering the row and column directions as opposed to the ...

In the present contribution we tackle the problem of nonlinear independent component analysis by non-Euclidean Hebbian-like learning. Independent component analysis (ICA) and blind source separation originally were introduced as tools for the linear unmixing of the signals to detect the underlying sources. Hebbian methods became very popular and succesfully in this context. Many nonlinear ICA e...

Independent component analysis (ICA) is a popular unsupervised learning method. This paper extends it to multilinear modewise ICA (MMICA) for tensors and explores two architectures in learning and recognition. MMICA models tensor data as mixtures generated from modewise source matrices that encode statistically independent information. Its sources have more compact representations than the sour...

Although hyperspectral images provide abundant information about objects, their high dimensionality also substantially increases computational burden. Dimensionality reduction offers one approach to Hyperspectral Image (HSI) analysis. Currently, there are two methods to reduce the dimension, band selection and feature extraction. In this paper, we present a band selection method based on Indepe...

The performance of unsupervised learning models for natural images is evaluated quantitatively by means of information theory. We estimate the gain in statistical independence (the multi-information reduction) achieved with independent component analysis (ICA), principal component analysis (PCA), zero-phase whitening, and predictive coding. Predictive coding is translated into the transform cod...

Accurately evaluating statistical independence among random variables is a key component of Independent Component Analysis (ICA). In this paper, we employ a squared-loss variant of mutual information as an independence measure and give its estimation method. Our basic idea is to estimate the ratio of probability densities directly without going through density estimation, by which a hard task o...

Abstract: Given a set of M signal mixtures (x1, x2, . . . , xM ) (e.g. microphone outputs), each of which is a different mixture of a set of M statistically independent source signals (s1, s2, . . . , sM ) (e.g. voices), independent component analysis (ICA) recovers the source signals (voices) from the signal mixtures. ICA is based on the assumptions that source signals are statistically indepe...

-----------------------------------------------------------------------------ABSTRACT------------------------------------------------------Independent Component Analysis (ICA) is the decomposition technique of a random vector of data into linear components which are “independent as possible.” Involves finding a suitable representation of multivariate data for computational and conceptual simpli...

Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal ord...