Finding dependent and independent components from related data sets: A generalized canonical correlation analysis based method

Karhunen, Juha; Hao, Tele; Ylipaavalniemi, Jarkko

doi:10.1016/j.neucom.2013.01.018

Cited by 8 publications

(6 citation statements)

References 22 publications

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“…In our context, such a rotation would, however, be suboptimal since rotating sparse independent components decreases their sparsity. In very recent work [40] , the authors reversed the order of analysis (first analysis across the data sets, then finding independent sources within each data set). In our context, such an approach would, however, also be suboptimal since it does not seem to yield coupled canonical coordinates but only coupled subspaces.…”

Section: Discussionmentioning

confidence: 99%

Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

et al. 2014

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Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlation analysis: It finds independent components in each data set which, across the two data sets, are related to each other via linear or higher-order correlations. The new method is as widely applicable as canonical correlation analysis, and also to more than two data sets. We call it higher-order canonical correlation analysis. When applied to chromatic natural images, we found that it provides a single (unified) statistical framework which accounts for both spatio-chromatic processing and adaptation. Filters with spatio-chromatic tuning properties as in the primary visual cortex emerged and corresponding-colors psychophysics was reproduced reasonably well. We used the new method to make a theory-driven testable prediction on how the neural response to colored patterns should change when the illumination changes. We predict shifts in the responses which are comparable to the shifts reported for chromatic contrast habituation.

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Section: Discussionmentioning

confidence: 99%

Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

et al. 2014

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“…Data-driven methods such as MCCA are attractive in that they find a mapping between subjects based only on shared temporal aspects of the data, without 33 requiring external information. MCCA and related methods have been widely used for fMRI data (Li et al, 2009;Correa et al, 2010b;Hwang et al, 2012;Afshin-Pour et al, 2012;Karhunen et al, 2013;Haxby et al, 2011;Afshin-Pour et al, 2014) and EEG/MEG (Lankinen et al, 2014;Sturm, 2016;Zhang et al, 2017). In contrast to MCCA, which finds variance dimensions that are similar across subjects with no attempt to ensure that they correspond to sources within the brain, ICA-based approaches attempt to to isolate sources common across subjects based on criteria of statistical independence (Calhoun and Adali, 2012;Eichele et al, 2011;Huster et al, 2015;Chen et al, 2016;Madsen et al;Huster and Raud, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…CCA has been used extensively for brain data analysis and modality fusion (Sui et al, 2012;Dähne et al, 2015;Dmochowski et al, 2017), and several studies have applied multiway CCA (MCCA) and variants thereof to merge data across subjects (Correa et al, 2010b;Afshin-Pour et al, 2012Lankinen et al, 2014;Zhang et al, 2017;Li et al, 2009;Hwang et al, 2012;Karhunen et al, 2013;Haxby et al, 2011;Lankinen et al, 2014;Sturm, 2016;Zhang et al, 2017;Lankinen et al, 2018). This paper builds on those studies with the aim to better understand the range of applicability of the tool, what is achieved, and what are the caveats.…”

Section: Introductionmentioning

confidence: 99%

Multiway canonical correlation analysis of brain data

et al. 2019

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“…Data-driven methods such as MCCA are attractive in that they find a mapping between subjects based only on shared temporal aspects of the data, without requiring external information. MCCA and related methods have been widely used for fMRI data (Li et al, 2009; Correa et al, 2010b; Hwang et al, 2012; Afshin-Pour et al, 2012; Karhunen et al, 2013; Haxby et al, 2011; Afshin-Pour et al, 2014) and EEG/MEG (Lankinen et al, 2014; Sturm, 2016; Zhang et al, 2017). In contrast to MCCA, which finds variance dimensions that are similar across subjects with no attempt to ensure that they correspond to sources within the brain, ICA-based approaches attempt to to isolate sources common across subjects based on criteria of statistical independence (Calhoun and Adali, 2012; Eichele et al, 2011; Huster et al, 2015; Chen et al, 2016; Madsen et al; Huster and Raud, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…CCA has been used extensively for brain data analysis and modality fusion (Sui et al, 2012; Dähne et al, 2015; Dmochowski et al, 2017), and several studies have applied multiway CCA (MCCA) and variants thereof to merge data across subjects (Correa et al, 2010b; Afshin-Pour et al, 2012, 2014; Lankinen et al, 2014; Zhang et al, 2017; Li et al, 2009; Hwang et al, 2012; Karhunen et al, 2013; Haxby et al, 2011; Lankinen et al, 2014; Sturm, 2016; Zhang et al, 2017; Lankinen et al, 2018). This paper builds on those studies with the aim to better understand the range of applicability of the tool, what is achieved, and what are the caveats.…”

Section: Introductionmentioning

confidence: 99%

Multiway Canonical Correlation Analysis of Brain Signals

Cheveigné

Liberto

Arzounian

et al. 2018

Preprint

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Brain signals recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratio due to the presence of multiple competing sources and artifacts. A common remedy is to average over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with the same identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method.

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Finding dependent and independent components from related data sets: A generalized canonical correlation analysis based method

Cited by 8 publications

References 22 publications

Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

Multiway canonical correlation analysis of brain data

Multiway Canonical Correlation Analysis of Brain Signals

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