Independent Component Analysis: Blind Source Separation

Independent Component Analysis (ICA) is a technique used since middle 80s, and due to all its applications, it has been a common research topic. Simplifying the concept, with the ICA technique we can separate multivariate additive signals. Despite that there are other methods to do so, ICA can do it without knowing nothing (or barely nothing) of the signals and context. Along this thesis the basic algorithm for Independent Component Analysis will be explained. It is called FastICA and was invented by Aapo Hyvärinen as a simply and versatile algorithm with a scheme of fixed-point iterations. This means an algorithm that search the convergence of a vector with iterations, similar to the Newton’s method. This technique is not that simple though, the mathematic and theoretical background is quite complex. But in order to understand how the algorithm works, all of the concepts will be explained step by step.Boluda Burguete, V. (2015). Independent Component Analysis: Blind Source Separation. Universitat Politècnica de València. http://hdl.handle.net/10251/57400TFG

Information Theoretical Estimators Toolbox

We present ITE (information theoretical estimators) a free and open source, multi-platform, Matlab/Octave toolbox that is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities, and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems. ITE also includes a prototype application in a central problem class of signal processing, independent subspace analysis and its extensions.Comment: 5 pages; ITE toolbox: https://bitbucket.org/szzoli/ite

Independent Component Analysis for Group Comparison of Functional MRI Images in Individuals with Parkinson's Disease

Parkinson's Disease (PD) is an age-related disorder that affects cognitive and motor abilities and lowers quality of life. As there is currently no cure, it is an area of interest for many research efforts. Parkinson's disease has a substantial effect on structures in the basal ganglia, which may be used to indicate signs of Parkinson's disease progression. Functional MRI (fMRI) is a means of measuring metabolic functioning in the brain. Brain imaging studies are not used to diagnose Parkinson's disease because it is unclear how it manifests in neuroimages. However, Parkinson's disease has a preclinical phase during which structures within the brain are affected, but external symptoms have not yet manifested. In this study, we sought to identify effects of Parkinson's disease that may be seen in functional imaging scans to allow earlier detection. We used independent component analysis (ICA) to identify functional brain networks followed by dual regression to estimate subject-level components for making comparisons of the functional images between the two groups: a group of subjects with Parkinson's disease and a group of healthy control subjects. We were able to generate subject-level components; however, identifying one component at the group level that included the basal ganglia proved problematic. Methods of identifying neural structures within the application we used provided conflicting evidence. Therefore, we were unable to determine if differences between the study groups existed that could be seen in functional imaging scans. Testing our primary endpoint using a voxel-wise linear regression with each of the components was not successful because most of the p values on the coefficient of interest were non-significant. In addition, there was a poor model fit seen in the regression models. We were unable to provide scientific evidence of differences that might be seen in functional MRI studies between subjects with Parkinson's disease and healthy control subjects

Application of independent component analysis for identification of speakers from speech recordings

U ovom radu je na snimke više govornika primijenjena metoda nezavisnih komponenata (FastICA algoritam u Matlabu) u svrhu izdvajanja izvornih signala. Kada bi se izvorni signali mogli uspješno izdvojiti iz bilo koje mješavine signala to bi mogli primijeniti na niz različitih problema, npr. omogućili bi slušnim aparatima da raspoznaju različite govornike i fokusiraju se na određenog govornika, što je poznato pod nazivom 'cocktail party problem'. Dobiveni rezultati pokazuju da ova metoda funkcionira na snimkama gdje je broj govornika jednak broju mikrofona uz pretpostavku da su izlazne vrijednosti mikrofona linearne mješavine nezavisnih komponenti. Ljudski govor je sam po sebi jako kompleksan, a to posebno dolazi do izražaja kad istovremeno govori više govornika. Otkrivanje mehanizma selektivne pozornosti u mozgu je ključ za rješavanje 'cocktail party problem'.In this paper was applied independent component analysis (FastICA algorithm in Matlab) on the records of more speakers for the purpose of extracting the original signals. If the source signals could be successfully extracted from any signal mixture they could apply to a variety of different problems, for example, allow hearing aids to discern different speakers and focus on a particular speaker, known as the 'cocktail party problem'. The results obtained show that this method works on experimental recordings where the number of speakers is equal to the number of microphones assuming that the output values of the microphone are linear mixtures of independent components. Human speech is very complex by itself, and this is particularly pronounced when multiple speakers speak simultaneously. Detecting a selective attention mechanism in the brain is the key to solving the 'cocktail party problem'

Application of independent component analysis for identification of speakers from speech recordings

U ovom radu je na snimke više govornika primijenjena metoda nezavisnih komponenata (FastICA algoritam u Matlabu) u svrhu izdvajanja izvornih signala. Kada bi se izvorni signali mogli uspješno izdvojiti iz bilo koje mješavine signala to bi mogli primijeniti na niz različitih problema, npr. omogućili bi slušnim aparatima da raspoznaju različite govornike i fokusiraju se na određenog govornika, što je poznato pod nazivom 'cocktail party problem'. Dobiveni rezultati pokazuju da ova metoda funkcionira na snimkama gdje je broj govornika jednak broju mikrofona uz pretpostavku da su izlazne vrijednosti mikrofona linearne mješavine nezavisnih komponenti. Ljudski govor je sam po sebi jako kompleksan, a to posebno dolazi do izražaja kad istovremeno govori više govornika. Otkrivanje mehanizma selektivne pozornosti u mozgu je ključ za rješavanje 'cocktail party problem'.In this paper was applied independent component analysis (FastICA algorithm in Matlab) on the records of more speakers for the purpose of extracting the original signals. If the source signals could be successfully extracted from any signal mixture they could apply to a variety of different problems, for example, allow hearing aids to discern different speakers and focus on a particular speaker, known as the 'cocktail party problem'. The results obtained show that this method works on experimental recordings where the number of speakers is equal to the number of microphones assuming that the output values of the microphone are linear mixtures of independent components. Human speech is very complex by itself, and this is particularly pronounced when multiple speakers speak simultaneously. Detecting a selective attention mechanism in the brain is the key to solving the 'cocktail party problem'

Advances in independent component analysis with applications to data mining

This thesis considers the problem of finding latent structure in high dimensional data. It is assumed that the observed data are generated by unknown latent variables and their interactions. The task is to find these latent variables and the way they interact, given the observed data only. It is assumed that the latent variables do not depend on each other but act independently. A popular method for solving the above problem is independent component analysis (ICA). It is a statistical method for expressing a set of multidimensional observations as a combination of unknown latent variables that are statistically independent of each other. Starting from ICA, several methods of estimating the latent structure in different problem settings are derived and presented in this thesis. An ICA algorithm for analyzing complex valued signals is given; a way of using ICA in the context of regression is discussed; and an ICA-type algorithm is used for analyzing the topics in dynamically changing text data. In addition to ICA-type methods, two algorithms are given for estimating the latent structure in binary valued data. Experimental results are given on all of the presented methods. Another, partially overlapping problem considered in this thesis is dimensionality reduction. Empirical validation is given on a computationally simple method called random projection: it does not introduce severe distortions in the data. It is also proposed that random projection could be used as a preprocessing method prior to ICA, and experimental results are shown to support this claim. This thesis also contains several literature surveys on various aspects of finding the latent structure in high dimensional data.reviewe