In the independent component (IC) model it is assumed that the components of the observed p-variate random vector x are linear combinations of the components of a latent p-vector z such that the p components of z are independent. Then x = Ωz where Ω is a full-rank p× p mixing matrix. In the independent component analysis (ICA) the aim is to estimate an unmixing matrix Γ such that Γx has indepen...

In independent component analysis (ICA), linear transformation that minimizes the dependence among the components is estimated. Conventional ICA algorithms are applicable when the numbers of sources and observations are equal; however, they are inapplicable to the underdetermined case where the number of sources is larger than that of observations. Most underdetermined ICA algorithms have been ...

In the recent years, a number of publications have appeared that deal with automatically calculating the similarity of music tracks. Most of them are based on features that are not intuitively understandable to humans, as they do not have a musically meaningful counterpart, but are merely measures of basic physical properties of the audio signal. Furthermore, most of these algorithms do not tak...

In this paper we present a novel method for integration the prediction results by finding common latent components via independent component analysis. The latent components can have constructive or destructive influence on particular prediction results. After the elimination of the deconstructive signals we rebuilt the improved predictions. We check the method validity on the electricity load p...

Recently, the score function difference (SFD) has been applied to develop ICA algorithms. But such algorithms are not suitable for highdimensional data because the SFD estimation in a high-dimensional space is problematic. In this paper, by investigating the relationship between mutual independence and pairwise independence, we develop an approach for ICA with linear instantaneous mixtures and ...

Respiration signal of the vital signs is an important parameter in clinical parts. To extract the respiration signal from PPG signal for mobile healthcare system is difficult because the bands of the motion artifacts and respiration in the frequency domain are overlapped. This study to improve this problem suggested a respiration extraction method using the independent component analysis and ev...

We present a method for audio-visual classification of Swedish phonemes, to be used in computer-assisted pronunciation training. The probabilistic kernel-based method is applied to the audio signal and/or either a principal or an independent component (PCA or ICA) representation of the mouth region in video images. We investigate which representation (PCA or ICA) that may be most suitable and t...

For any field F, the set of all functions f : V (G) → F whose sum on each maximal independent set is constant forms a vector space over F. In this paper, we show that the dimension can vary depending on the characteristic of the field. We also investigate the dimensions of these vector spaces and show that while some families, such as chordal graphs, have unbounded dimension, other families, su...

The problem of separating audio sources observed in a real room environment is a very challenging task, also known as the cocktail party problem. Much work has been presented on audio separation, even in cases of high reverb. However, various problems remain unsolved in a real-world scenario. In this paper, the authors review proposed solutions employing Independent Component analysis (ICA), di...

Several neural algorithms for Independent Component Analysis (ICA) have been introduced lately, but their computational properties have not yet been systematically studied. In this paper, we compare the accuracy , convergence speed, computational load, and other properties of ve prominent neural or semi-neural ICA algorithms. The comparison reveals some interesting diierences between the algori...