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Independent component analysis (ICA) is a method for recovering statistically independent signals from observations of unknown linear combinations of the sources. ... of random vectors. ... RANDOMIZED INDEPENDENT COMPONENT ANALYSIS As demonstrated in  , the kernel canonical correlation and the kernel generalized variance measure the statistical dependence between a set of sampled random ...arXiv:1609.06942v1 fatcat:z6uxw2mdevekdnnkuxe42gatgq
Time-lagged independent component analysis (tICA) is a widely used dimension reduction method for the analysis of molecular dynamics (MD) trajectories and has proven particularly useful for the construction ... In particular, and contrary to previous studies of principal component projections, the projections change non-continuously with increasing lag time. ... Acknowledgements We thank Nicolai Kozlowski, Malte Schäffner and Andreas Volkhardt for very helpful discussions; and Andreas Volkhardt for providing the MD-trajectories for our analysis. ...doi:10.1101/2021.03.18.435940 fatcat:vjtuig3kbvbtfez2p2lhjae3ee
Component Analysis (TICA). ... geometric/mechanistic approaches which disregard the network function, a clear exception to this trend in the literature is the original approach of Hyvärinen and Hoyer based on infomax and Topographic Independent ... Under this functional constraint, the linear Independent Component Analysis (ICA) is able to explain the shape of the receptive fields  . ...doi:10.1371/journal.pone.0178345 pmid:28640816 pmcid:PMC5480835 fatcat:7ffndsopkngjzjvsnti6cnnhlu
An intelligent novel model comprising Random Forest Classifier with Independent Component Analysis (ICA) was proposed for botnet detection in IoT devices. ... Various machine learning algorithms were also implemented upon the processed data for comparative analysis. ... Component Analysis for detecting botnets. ...doi:10.32890/jict2022.21.2.3 fatcat:2wodmgb54zgkhopk2pjnx6dg3i
This paper proposes a random forest and modified independent component analysis (RF-MICA) to detect the occurrence of PV faults. ... The MICA was developed for dimensionality reduction for enhanced performance, whereas previous studies only focused on principal component analysis. ... A PV shading fault was investigated using principal component analysis (PCA)  . Although a simple method was offered, the results were unsatisfactory. ...doi:10.1109/access.2022.3166477 fatcat:yyswlk3oyneqdccqbagi6rhkui
A twostep method is presented (i) Independent Component Analysis for the detection of inter-area modes and estimation of their frequencies, and (ii) Random Decrement for the estimation of mode damping. ... This paper presents a novel approach to the monitoring of inter-area oscillation frequency and damping using multivariate analysis techniques. ... Independent Component Analysis (ICA) Independent Component Analysis (ICA) is a method used to solve a problem known as blind source separation which refers to the process of recovering unobserved signals ...doi:10.1109/tpwrs.2010.2050607 fatcat:pcfydcl3avc67pe54ucsfmluxy
SEG Technical Program Expanded Abstracts 2017
A conventional method for deblending is independent component analysis (ICA), which assumes a "cocktail-party" mixing model where each receiver records a linear combination of source signals assumed to ... be statistically independent, and where only one source can have a Gaussian distribution. ... uses independent component analysis (ICA) to separate the active and drill-bit source signals. ...doi:10.1190/segam2017-17677817.1 fatcat:ftlvp6tfanhkndcdlqube4qpvq
By performing an analysis of the kurtosis estimator, we find the maximum reduction rate which guarantees a narrow confidence interval of such estimator with high confidence level. ... Introduction Independent Component Analysis (ICA) ( [1, 2, 3, 4] ) is a method to identify a set of unknown and generally non-Gaussian source signals whose mixtures are observed, under the only assumption ... that they are mutually independent. ...doi:10.1007/978-3-540-74494-8_24 dblp:conf/ica/GaitoG07 fatcat:d72ohkiizrcrrppaerx3dntm3i
In this paper, we introduce a conjugate (inverse-) gamma Markov Random field model that allows random fluctuations on variances which are useful as priors for nonstationary time-frequency energy distributions ... We assume that J = 2 components are generated independently by two IGMRF models with vertical and horizontal topology. ... In figure 3 -(b), we observe that the model is able to separate transients and harmonic components. ...doi:10.1007/978-3-540-74494-8_87 dblp:conf/ica/CemgilD07 fatcat:qcrhsz5ocjfefnw6oc2dralsva
"mixing matrix". • The s i are hidden random factors called "independent components", or "source signals" • Problem: Estimate both a i j and s j , observing only x i . ... of components Comparison of ICA, factor analysis and principal component analysis • ICA is nongaussian FA with no separate noise or specific factors. ...doi:10.1016/j.sigpro.2007.03.013 fatcat:wcbmnjtwfbeutj2ummm7pqfemq
The paper defines a multidimensional conditional linear random process, each component of which is represented as a stochastic integral of a random kernel driven by a process with independent increments ... independent random impulses that occur at Poisson moments. ... but the components of such a signal are driven by separate independent processes. ...doi:10.15587/2706-5448.2022.259906 doaj:f48abc79a0a44b9abb1d41da6c082107 fatcat:abfycgve7zba5pdxhriwojvnlu
However, in the data analysis, Wj( = 1, corre.sponding to the random intercept because the estimated variance component of the random slope is negligible. ... All random effects parameters are independent standard normal random variables. ...
Behavior Research Methods
Ikhav Res (2012) 44:1239 1243 1241 Table 1 Dimension profiles by independent component analysis Independent Component Analysis Principal Component Analysis and principal component Component 1 C'omponcnt ... The mutual in¬ formation is zero if and only if the random variables are statistically independent. Thus. ICs can be derived by mini¬ mizing the mutual information among the components. ...
Medical Imaging 2002: Ultrasonic Imaging and Signal Processing
A blind source separation problem is formulated to discern the different signal components for correct interpretation of the data using independent component analysis (ICA). ... Then, given a region of interest, the temporal signals corresponding to all pixels within this region undergo the ICA iteration to compute a set of independent signals that most represent the actual components ... INDEPENDENT COMPONENT ANALYSIS As mentioned above, the independent component analysis (ICA) belongs to a class of techniques that are commonly termed blind source separation techniques. ...doi:10.1117/12.462158 fatcat:s3hbwp7r5jfzrnwinrookx2x6a
Here, we use the independent components analysis (ICA) method to separate the clean data from the rest of the sources, artifacts within the brain. ... The separated independent components are good, we will clear the indpendent components is not brain activity sources and get EEG signals without the artifacts. I. ... Then, we use the independent component analysis method to obtain independent components. The "corrected" EEG signals obtained by removing the artifact independent components from the data. B. ...doi:10.1007/978-3-642-12020-6_72 fatcat:vmjxpcuhynazzgglyckairvl6m
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