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Independent Component Analysis with Functional Neuroscience Data Analysis [PDF]
Journal of Biomedical Physics and Engineering, 2023 Background: Independent Component Analysis (ICA) is the most common and standard technique used in functional neuroscience data analysis. Objective: In this study, two of the significant functional brain techniques are introduced as a model for ...
Hadeel K Aljobouri
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Independent component analysis algorithms for non-invasive fetal electrocardiography. [PDF]
PLoS ONE, 2023 The independent component analysis (ICA) based methods are among the most prevalent techniques used for non-invasive fetal electrocardiogram (NI-fECG) processing. Often, these methods are combined with other methods, such adaptive algorithms.
Rene Jaros +5 more
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Scatter Matrices and Independent Component Analysis
Austrian Journal of Statistics, 2016 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.
Hannu Oja, Seija Sirkiä, Jan Eriksson
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robustica: customizable robust independent component analysis [PDF]
BMC Bioinformatics, 2022 Background Independent Component Analysis (ICA) allows the dissection of omic datasets into modules that help to interpret global molecular signatures.
Miquel Anglada-Girotto +3 more
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Independent component analysis: An introduction [PDF]
Applied Computing and Informatics, 2021 Independent component analysis (ICA) is a widely-used blind source separation technique. ICA has been applied to many applications. ICA is usually utilized as a black box, without understanding its internal details.
Alaa Tharwat
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BOTNET DETECTION USING INDEPENDENT COMPONENT ANALYSIS
International Islamic University Malaysia Engineering Journal, 2022 Botnet is a significant cyber threat that continues to evolve. Botmasters continue to improve the security framework strategy for botnets to go undetected.
Wan Nurhidayah Ibrahim +3 more
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Topographic Independent Component Analysis [PDF]
Neural Computation, 2001 In ordinary independent component analysis, the components are assumed to be completely independent, and they do not necessarily have any meaningful order relationships. In practice, however, the estimated “independent” components are often not at all independent. We propose that this residual dependence structure could be used to define a topo-graphic
Hyvärinen, Aapo +2 more
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Principal independent component analysis [PDF]
IEEE Transactions on Neural Networks, 1999 Conventional blind signal separation algorithms do not adopt any asymmetric information of the input sources, thus the convergence point of a single output is always unpredictable. However, in most of the applications, we are usually interested in only one or two of the source signals and prior information is almost always available.
J, Luo, B, Hu, X T, Ling, R W, Liu
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Independent nonlinear component analysis [PDF]
Journal of the American Statistical Association, 2019 The idea of summarizing the information contained in a large number of variables by a small number of "factors" or "principal components" has been broadly adopted in economics and statistics. This paper introduces a generalization of the widely used principal component analysis (PCA) to nonlinear settings, thus providing a new tool for dimension ...
Gunsilius, Florian +1 more
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Independent component analysis in spiking neurons. [PDF]
PLoS Computational Biology, 2010 Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such ...
Cristina Savin +2 more
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