In the independent component analysis (ICA) it is assumed that the components of the multivariate independent and identically distributed observations are linear transformations of latent independent components. The problem then is to find the (linear) transformation which transforms the observations back to independent components. In the paper the ICA is discussed and it is shown that, under s...

Abstract Funding Acknowledgements Type of funding sources: None. Background Computed Tomography Coronary Angiography (CTCA) is increasingly being used to detect and exclude the presence coronary artery stenosis. Published studies date comparing CTCA invasive angiography (ICA) have focused on epicardial vessels other than left main stem (LMS) (1 - 4). Despite diagnostic accuracy specifically for...

A new two-stage blind source separation (BSS) method for convolutive mixtures of speech is proposed, in which a single-input multiple-output (SIMO)-model-based independent component analysis (ICA) and a new SIMO-model-based binary masking are combined. SIMO-model-based ICA enables us to separate the mixed signals, not into monaural source signals but into SIMOmodel-based signals from independen...

A geometric approach to data representation incorporating informationtheoretic ideas is presented. The task of finding a faithful representation, where the input distribution is evenly partitioned into regions of equal mass, is addressed. For input consisting of mixtures of statistically independent sources, we treat independent component analysis (ICA) as a computational geometry problem. Firs...

Independent component analysis (ICA) has proven useful for modeling brain and electroencephalographic (EEG) data. Here, we present a new, generalized method to better capture the dynamics of brain signals than previous ICA algorithms. We regard EEG sources as eliciting spatio-temporal activity patterns, corresponding to, e.g. trajectories of activation propagating across cortex. This leads to a...

We propose a method to enhance the quality and precision of prediction results using measurements in the context of radio network modelling. The proposed method involves the use of an Independent Component Analysis (ICA) block and a MultiLayer Perceptron (MLP) Artificial Neural Network (ANN). The role of the ICA block is to make the variables at the input of the ANN statistically independent so...

Abstract: Based on the fast kernel entropy optimization independent component analysis and the minimum conditional entropy, this paper proposes a harmonic source localization method which aims at accurately estimating harmonic currents and identifying harmonic sources. The injected harmonic currents are estimated by the fast kernel entropy optimization independent component analysis (FKEO-ICA) ...

Functional magnetic resonance imaging (fMRI) of temporally-coherent blood oxygenization leveldependent (BOLD) signal provides an effective means of analyzing functionally coherent patterns in the brain [6, 5, 13]. Intrinsic networks [INs, 3] and functional connectivity are important outcomes of fMRI studies and are central to understanding brain function and making diagnoses [4, 1, 10]. The mos...

This work proposes a novel algorithm for independent component analysis (ICA) based on marginal density estimation. The proposed ICA algorithm aims to search for an effective demixing matrix as well as weighted Parzen window (WPW) representations for marginal densities of independent components so as to express a factorial joint density for high dimensional observations. Following the linear mi...

Principal component analysis (PCA) and independent component analysis (ICA) are both based on a linear model of multivariate data. They are often seen as complementary tools, PCA providing dimension reduction and ICA separating underlying components or sources. In practice, a two-stage approach is often followed, where first PCA and then ICA is applied. Here, we show how PCA and ICA can be seen...