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Independent factor discriminant analysis

Angela Montanari, Daniela G. Calò, Cinzia Viroli
<span title="">2008</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/akgkzs3bjzgpdixvnrfpwqkvki" style="color: black;">Computational Statistics &amp; Data Analysis</a> </i> &nbsp;
Independent factor analysis is in fact a generative latent variable model whose structure closely resembles the one of ordinary factor model but it assumes that the latent variables are mutually independent  ...  In the general classification context the recourse to the so-called Bayes decision rule requires to estimate the class conditional probability density functions.  ...  Exploiting the independence condition they rephrase the multivariate density estimation task as a sequence of univariate ones, i.e. the estimation of the marginal densities, whose product yields the multivariate  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.csda.2007.09.026">doi:10.1016/j.csda.2007.09.026</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vicfovotxjcsvkniqvlqcr26cm">fatcat:vicfovotxjcsvkniqvlqcr26cm</a> </span>
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Modeling multivariable hydrological series: Principal component analysis or independent component analysis?

Seth Westra, Casey Brown, Upmanu Lall, Ashish Sharma
<span title="">2007</span> <i title="American Geophysical Union (AGU)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xxkwcnneurcmbgvaukudmamcgu" style="color: black;">Water Resources Research</a> </i> &nbsp;
The transformation is achieved through a technique known as independent component analysis (ICA), which uses an approximation of mutual information to maximize the independence between the transformed  ...  Sharma (2007), Modeling multivariable hydrological series: Principal component analysis or independent component analysis?, Water Resour. Res., 43, W06429,  ...  Estimating the Independent Components [18] As mentioned previously, the objective of ICA is to find projections which yield components that are as independent as possible, where independence means that  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1029/2006wr005617">doi:10.1029/2006wr005617</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cr2cpcj665h5xfvuvfxxjbsuim">fatcat:cr2cpcj665h5xfvuvfxxjbsuim</a> </span>
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Independent Component Discriminant Analysis for hyperspectral image classification

A Villa, J A Benediktsson, J Chanussot, C Jutten
<span title="">2010</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ao54s6nulvbsdnbbncuz4yl4gi" style="color: black;">2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing</a> </i> &nbsp;
The method is based on the use of Independent Component Analysis (ICA) to choose a transform matrix so that the transformed components are as independent as possible.  ...  Then, a non parametric estimation of the density function is computed for each independent component. Finally, the Bayes rule is applied for classification assignment.  ...  INDEPENDENT COMPONENT DISCRIMINANT ANALYSIS The proposed method is a generalization of the quadratic discriminant analysis, where the ability of ICA to retrieve independent components is exploited to estimate  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/whispers.2010.5594853">doi:10.1109/whispers.2010.5594853</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/whispers/VillaBCJ10.html">dblp:conf/whispers/VillaBCJ10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zwgbsdk6fvb5pnrpe272rvi3xe">fatcat:zwgbsdk6fvb5pnrpe272rvi3xe</a> </span>
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A. Villa, J.A. Benediktsson, J. Chanussot, C. Jutten
<span title="">2010</span> <i title="IEEE"> 2010 20th International Crimean Conference &#34;Microwave &amp; Telecommunication Technology&#34; </i> &nbsp;
The method is based on the use of Independent Component Analysis (ICA) to choose a transform matrix so that the transformed components are as independent as possible.  ...  Then, a non parametric estimation of the density function is computed for each independent component. Finally, the Bayes rule is applied for classification assignment.  ...  INDEPENDENT COMPONENT DISCRIMINANT ANALYSIS The proposed method is a generalization of the quadratic discriminant analysis, where the ability of ICA to retrieve independent components is exploited to estimate  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/crmico.2010.5632389">doi:10.1109/crmico.2010.5632389</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/v657jjz3ujhglegopt46irkc4q">fatcat:v657jjz3ujhglegopt46irkc4q</a> </span>
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Likelihood-based population independent component analysis

Ani Eloyan, Ciprian M. Crainiceanu, Brian S. Caffo
<span title="2013-01-10">2013</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/d2xjsctbnvayjlur7x2f46qpxy" style="color: black;">Biostatistics</a> </i> &nbsp;
Independent component analysis (ICA) is a widely used technique for blind source separation, used heavily in several scientific research areas including acoustics, electrophysiology and functional neuroimaging  ...  The method is based on likelihood estimators of the underlying source densities and the mixing matrix.  ...  The data are generated by the ICA model X i = A i S with T as the densities for independent components are shown inFigure 1.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/biostatistics/kxs055">doi:10.1093/biostatistics/kxs055</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/23314416">pmid:23314416</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3677736/">pmcid:PMC3677736</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/z43xopo6zvhw7lmyq5nply24ea">fatcat:z43xopo6zvhw7lmyq5nply24ea</a> </span>
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Nonlinear Prediction Based on Independent Component Analysis Mixture Modelling [chapter]

Gonzalo Safont, Addisson Salazar, Luis Vergara
<span title="">2011</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Nonlinear prediction based on independent component analysis mixture modelling. Abstract.  ...  This paper presents a new algorithm for nonlinear prediction based on independent component analysis mixture modelling (ICAMM).  ...  This paper presents a novel procedure based on independent component analysis mixture modelling (ICAMM).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-21498-1_64">doi:10.1007/978-3-642-21498-1_64</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s5ocxvd4vfbzxgzxvhq5idaqma">fatcat:s5ocxvd4vfbzxgzxvhq5idaqma</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180722193143/https://riunet.upv.es/bitstream/handle/10251/58589/IWANN%20_Pred_v11_RiuNet.pdf;jsessionid=D2E97C2162526FEDB0B364BC936725A8?sequence=3" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2e/3d/2e3d7cdc69a41931f3ff3236a1b19250291245c3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-21498-1_64"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

​Rank based Least-squares Independent Component Analysis

Jafar Rahmanishamsi, Ali Dolati
<span title="2018-03-01">2018</span> <i title="Armenian Green Publishing Co."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zgiunwux6nhrhdayw4vmtz4usm" style="color: black;">Journal of Statistical Research of Iran</a> </i> &nbsp;
In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed.  ...  The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version.  ...  ICA can be viewed as a generalization of the principal component analysis (PCA). PCA decorrelates original data so that the sample covariance of obtained components is zero.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.29252/jsri.14.2.247">doi:10.29252/jsri.14.2.247</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d5yi5m3s6jhkjfk3aonu7eti3i">fatcat:d5yi5m3s6jhkjfk3aonu7eti3i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180722000254/http://jsri.srtc.ac.ir/files/site1/user_files_7b5cd7/adolati-A-10-73-2-ceadd0c.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ea/f9/eaf9dcb1786cb570411f49cdccc346215082f3cd.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.29252/jsri.14.2.247"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Segmentation of magnetic resonance brain images through discriminant analysis

Umberto Amato, Michele Larobina, Anestis Antoniadis, Bruno Alfano
<span title="">2003</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/huhco7lwxvct3fbbxk44mmpflu" style="color: black;">Journal of Neuroscience Methods</a> </i> &nbsp;
on: (i) a transform of the images into components that are statistically independent from each other; (ii) a nonparametric estimate of probability density functions of each tissue starting from a training  ...  A comparison with parametric discriminant analysis is also reported.  ...  component discriminant analysis (PCDA), where original components are transformed into principal components prior to nonparametric density estimation; • Independent component discriminant analysis (ICDA  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0165-0270(03)00237-1">doi:10.1016/s0165-0270(03)00237-1</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/14659825">pmid:14659825</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/a3szsm2be5g6he475zvv6zi7ky">fatcat:a3szsm2be5g6he475zvv6zi7ky</a> </span>
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Nonlinear Independent Component Analysis by self-organizing maps [chapter]

Petteri Pajunen
<span title="">1996</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Linear Independent Component Analysis considers the problem of nding a linear transformation that makes the components of the output vector statistically independent.  ...  More generally we can consider nonlinear mappings that make the components of the output vectors independent.  ...  In 1] the concept of Independent Component Analysis (ICA) is formalized. A direct application of ICA is blind source separation (BSS), where source signals are recovered from their linear mixtures.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/3-540-61510-5_137">doi:10.1007/3-540-61510-5_137</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xwex7tsbf5akjnnrb6oz5oagoe">fatcat:xwex7tsbf5akjnnrb6oz5oagoe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190227144341/http://pdfs.semanticscholar.org/821f/66163ca34828189b31aa03dae53e39e0946c.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/82/1f/821f66163ca34828189b31aa03dae53e39e0946c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/3-540-61510-5_137"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Text Independent Speaker Identification using Integrating Independent Component Analysis with Generalized Gaussian Mixture Model

N M, Dr.V Sailaja, Dr.K. Srinivasa
<span title="">2011</span> <i title="The Science and Information Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2yzw5hsmlfa6bkafwsibbudu64" style="color: black;">International Journal of Advanced Computer Science and Applications</a> </i> &nbsp;
Hence, in this paper a new and novel Text Independent speaker identification model is developed by integrating MFCC's with Independent component analysis(ICA) for obtaining independency and to achieve  ...  Assuming that the new feature vectors follows a Generalized Gaussian Mixture Model (GGMM), the model parameters are estimated by using EM algorithm.  ...  component analysis.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2011.021213">doi:10.14569/ijacsa.2011.021213</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/erwdasrgtfax5jkfywqhn4o2t4">fatcat:erwdasrgtfax5jkfywqhn4o2t4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180720183637/http://thesai.org/Downloads/Volume2No12/Paper%2013-Text%20Independent%20Speaker%20Identification%20using%20Integrating%20Independent%20Component%20Analysis%20with%20Generalized%20Gaussian%20Mixture%20Model.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/85/f9/85f91890769fc7fcb0a72df45e17d0248a45cd1c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2011.021213"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

A robust approach to independent component analysis of signals with high-level noise measurements

Jianting Cao, N. Murata, S.-i. Amari, A. Cichocki, T. Takeda
<span title="">2003</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/22mhkeaq5zdqlmtti5oidj26fi" style="color: black;">IEEE Transactions on Neural Networks</a> </i> &nbsp;
Index Terms-Cross-validation method, independent component analysis (ICA), parametric estimation method, principal component analysis (PCA), robust prewhitening, t-distribution density model, unaveraged  ...  In this paper, we propose a robust approach for independent component analysis (ICA) of signals that observations are contaminated with high-level additive noise and/or outliers.  ...  It is assumed that the components of are mutually statistically independent, as well as statistically independent of the noise components .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnn.2002.806648">doi:10.1109/tnn.2002.806648</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/18238044">pmid:18238044</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rfiqfybumngmjggfmc5sybpjpm">fatcat:rfiqfybumngmjggfmc5sybpjpm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190224163108/http://pdfs.semanticscholar.org/5ac6/940f2a3048005d48aa231cab6a4dcba78ac0.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5a/c6/5ac6940f2a3048005d48aa231cab6a4dcba78ac0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnn.2002.806648"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

A Constrained EM Algorithm for Independent Component Analysis

Max Welling, Markus Weber
<span title="">2001</span> <i title="MIT Press - Journals"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rckx6fqoszfvva5c53bqivu5am" style="color: black;">Neural Computation</a> </i> &nbsp;
We introduce a novel way of performing independent component analysis using a constrained version of the expectation-maximization (EM) algorithm.  ...  We explain how our approach relates to independent factor analysis.  ...  From left to right, top to bottom, they are: principal component analysis, probabilistic principal component analysis, factor analysis, independent component analysis, EM-independent component analysis  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1162/089976601300014510">doi:10.1162/089976601300014510</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/11244561">pmid:11244561</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gsrslcrn3nedxirmzncjd56224">fatcat:gsrslcrn3nedxirmzncjd56224</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170812151718/http://authors.library.caltech.edu/27691/1/WELnc01.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/60/a9/60a95e99162af200294a14f3a52f6abb6b384e62.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1162/089976601300014510"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> mitpressjournals.org </button> </a>

Independent Component Analysis for Non-Normal Factor Analysis [chapter]

Aapo Hyvärinen, Yutaka Kano
<span title="">2003</span> <i title="Springer Japan"> New Developments in Psychometrics </i> &nbsp;
Independent component analysis (ICA) was developed in the signal processing and neural computation communities.  ...  On the other hand, the dimension of the observed data vector is often first reduced by principal component analysis, in which case ICA can be viewed as a method of determining the factor rotation using  ...  This is because factor analysis, or related techniques such as principal component analysis, can only estimate the factors up to a rotation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-4-431-66996-8_75">doi:10.1007/978-4-431-66996-8_75</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6mpb4fhdavebvoiwgbbx4bhsku">fatcat:6mpb4fhdavebvoiwgbbx4bhsku</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20060219122301/http://www.cis.hut.fi:80/aapo/ps/IMPS01.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/08/2a/082aaf415008154a66df69e9e068dd5a425ee2d9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-4-431-66996-8_75"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Application of Polynomial Spline Independent Component Analysis to fMRI Data [chapter]

Atsushi Kawaguchi, Young K., Xuemei Huang
<span title="2012-10-10">2012</span> <i title="InTech"> Independent Component Analysis for Audio and Biosignal Applications </i> &nbsp;
(Bordes et al., 2007) generalized it to semiparametric mixture models by using kernel density estimation.  ...  Our ICA on fMRI data is carried out by first reducing the number of independent components (IC) using tools such as principal component analysis (PCA) or singular value decomposition (SVD), followed with  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5772/50343">doi:10.5772/50343</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u77kdmipwfe37kiv53nwcoiehm">fatcat:u77kdmipwfe37kiv53nwcoiehm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190501095823/https://cdn.intechopen.com/pdfs/39848.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/da/84/da841a658ad2e6215b5ad240396a419d0a48e90b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5772/50343"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Topographic independent component analysis as a model of V1 organization and receptive fields

Aapo Hyvärinen, Patrik O Hoyer
<span title="">2001</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bby322qx6ndsje4ypr56c7nnly" style="color: black;">Neurocomputing</a> </i> &nbsp;
Independent component analysis (ICA) has been recently used as a model of natural image statistics and V1 simple cell receptive "elds.  ...  Here we show how to extend the ICA model to explain V1 topography as well.  ...  The function G has a similar role as the log-density of the independent components in classic ICA.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0925-2312(01)00490-8">doi:10.1016/s0925-2312(01)00490-8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/77iuhi56mraprh6xfezhdyunwm">fatcat:77iuhi56mraprh6xfezhdyunwm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170812084547/http://www.swindale.ecc.ubc.ca/MECH550/readings/class12?action=AttachFile&amp;do=get&amp;target=Hyvarinen+and+Hoyer+sparse+coding+maps+Neurocomputing.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/be/00/be00a29a4953e4851f73f9fe988e151b92ce9ec0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0925-2312(01)00490-8"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>
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