This study presents a modified infomax model of Independent Component Analysis (ICA) for the source separation problem of fMRI data. Functional MRI data is processed by different blind source separation techniques including Independent Component Analysis (ICA). ICA is a statistical decomposition method used for multivariate data source separation. ICA algorithm is based on independence of extra...

We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes from DNA microarray data. Based on an ICA mixture model of genomic expression patterns, linear and nonlinear ICA finds components that are specific to certain biological processes. Genes that exhibit significant up-regulation or down-regulation within each component are grouped into clusters. We t...

Introduction: The accuracy of analyzing Functional MRI (fMRI) data is usually decreases in the presence of noise and artifact sources. A common solution in for analyzing fMRI data having high noise is to use suitable preprocessing methods with the aim of data denoising. Some effects of preprocessing methods on the parametric methods such as general linear model (GLM) have previously been evalua...

With Independent Component Analysis (ICA) the objective is to separate multidimensional data into independent components. A well known problem in ICA is that since both the independent components and the separation matrix have to be estimated, neither the ordering nor the amplitudes of the components can be determined. One suggested method for solving these ambiguities in ICA is to measure the ...

This paper focuses on the application and comparison of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) using two generic artificially created datasets. PCA and ICA are assessed in terms of their abilities to infer reference spectra and to estimate relative concentrations of the constituent compounds present in the analysed samples. The results show that ICA outperfo...

Independent Component Analysis (ICA) is a recently developed, useful extension of standard Principal Component Analysis (PCA). The associated linear model is used mainly in source separation, where only the coeecients of the ICA expansion are of interest. In this paper, we propose a neural structure related to nonlinear PCA networks for estimating the basis vectors of ICA. This ICA network cons...

In this paper we consider the problem of dimensionality reduction techniques. Two techniques such as Independent Component analysis (ICA) and multidimensional latent semantic analysis (MDLSA) are proposed. A new document analysis method named multidimensional latent semantic analysis (MDLSA) which resolves the problem of in-depth document analysis, mines local information from a document effici...

The pandemic of 2020 brought a lot changes to the health and medical industry where smart devices started flowing in compensate lack hospitals for less severe cases. This work primarily focuses on lung abnormalities as lungs were one first organs break down when virus affected body. whilst not becoming but will act catalyst spread virus’s effect through thus concluding complete pulmonary breakd...

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input mul...

The over-complete case remains a difficult problem in the field of independent component analysis (ICA). In this article we combine a technique called “region of interest” (ROI) with a standard complete ICA. We show how to create a mask using ICA, then using the masked data for a second ICA. At the same time this method eliminates a commonly necessary model-based step in fMRI data analysis. We ...