Fast regularized canonical correlation analysis

Cruz‐Cano, Raul; Lee, Mei‐Ling Ting

doi:10.1016/j.csda.2013.09.020

Cited by 39 publications

(17 citation statements)

References 25 publications

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“…Tested parameters were NO 3 - , SO 4 2- , O 2 , Cl - , Na + , Mn (total) , Fe (total) , Br - , Mg 2+ , K + , Ca 2+ , Cu 2+ , Zn 2+ , PO 4 2- , and total organic carbon concentrations, temperature, conductivity, redox potential, pH, groundwater residence time, and depth. To address more directly how environmental parameters and individual OTUs were related, we performed a regularized Canonical Correlation Analysis (rCCA) between relative frequencies of OTUs and environmental parameters with the R package FRCC ( Cruz-Cano and Lee, 2014 ). We visualized results of the rCCA analysis using the approach developed by Patel et al (2010) A Network was built from the rCCA results.…”

Section: Methodsmentioning

confidence: 99%

Groundwater Isolation Governs Chemistry and Microbial Community Structure along Hydrologic Flowpaths

et al. 2015

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This study deals with the effects of hydrodynamic functioning of hard-rock aquifers on microbial communities. In hard-rock aquifers, the heterogeneous hydrologic circulation strongly constrains groundwater residence time, hydrochemistry, and nutrient supply. Here, residence time and a wide range of environmental factors were used to test the influence of groundwater circulation on active microbial community composition, assessed by high throughput sequencing of 16S rRNA. Groundwater of different ages was sampled along hydrogeologic paths or loops, in three contrasting hard-rock aquifers in Brittany (France). Microbial community composition was driven by groundwater residence time and hydrogeologic loop position. In recent groundwater, in the upper section of the aquifers or in their recharge zone, surface water inputs caused high nitrate concentration and the predominance of putative denitrifiers. Although denitrification does not seem to fully decrease nitrate concentrations due to low dissolved organic carbon concentrations, nitrate input has a major effect on microbial communities. The occurrence of taxa possibly associated with the application of organic fertilizers was also noticed. In ancient isolated groundwater, an ecosystem based on Fe(II)/Fe(III) and S/SO4 redox cycling was observed down to several 100 of meters below the surface. In this depth section, microbial communities were dominated by iron oxidizing bacteria belonging to Gallionellaceae. The latter were associated to old groundwater with high Fe concentrations mixed to a small but not null percentage of recent groundwater inducing oxygen concentrations below 2.5 mg/L. These two types of microbial community were observed in the three sites, independently of site geology and aquifer geometry, indicating hydrogeologic circulation exercises a major control on microbial communities.

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Section: Methodsmentioning

confidence: 99%

Groundwater Isolation Governs Chemistry and Microbial Community Structure along Hydrologic Flowpaths

et al. 2015

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“…Where is the covariance of the two datasets X and Y and and are the autocovariances, and is the components correlation. Ridge regularization can be performed on the neural data to prevent overfitting in CCA as follows (Vinod, 1976;Cruz-Cano and Lee, 2014;Leurgans et al, 1993;Bilenko and Gallant, 2016):…”

Section: Canonical Correlation Analysismentioning

confidence: 99%

Neurophysiological indices of audiovisual speech integration are enhanced at the phonetic level for speech in noise

O’Sullivan

Crosse

Liberto

et al. 2020

Preprint

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Seeing a speaker's face benefits speech comprehension, especially in challenging listening conditions. This perceptual benefit is thought to stem from the neural integration of visual and auditory speech at multiple stages of processing, whereby movement of a speaker's face provides temporal cues to auditory cortex, and articulatory information from the speaker's mouth can aid recognizing specific linguistic units (e.g., phonemes, syllables). However it remains unclear how the integration of these cues varies as a function of listening conditions. Here we sought to provide insight on these questions by examining EEG responses to natural audiovisual, audio, and visual speech in quiet and in noise.Specifically, we represented our speech stimuli in terms of their spectrograms and their phonetic features, and then Significance Statement During conversation, visual cues impact our perception of speech. Integration of auditory and visual speech is thought to occur at multiple stages of speech processing and vary flexibly depending on the listening conditions.Here we examine audiovisual integration at two stages of speech processing using the speech spectrogram and a phonetic representation, and test how audiovisual integration adapts to degraded listening conditions. We find significant integration at both of these stages regardless of listening conditions, and when the speech is noisy, we find enhanced integration at the phonetic stage of processing. These findings provide support for the multistage integration framework and demonstrate its flexibility in terms of a greater reliance on visual articulatory information in challenging listening conditions.

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“…(4) The rCCA method is accompanied with important disadvantages, it is computationally expensive, requires regularization parameter selection and leads to inaccurate solutions by filtering out important information [36]. To ease these drawbacks and to perform effective analysis of fMRI data using CCA, we regularize CCA by incorporating prior information about the low frequency fMRI signals by building a basis that better spans the subspace of fMRI signals.…”

Section: A Canonical Correlation Analysismentioning

confidence: 99%

A Projection CCA Method for Effective fMRI Data Analysis

Qadar

Seghouane

2019

IEEE Trans. Biomed. Eng.

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Objective: Canonical correlation analysis (CCA) is a data driven method that has been successfully used in functional magnetic resonance imaging (fMRI) data analysis. Standard CCA extracts meaningful information from a pair of data sets by seeking pairs of linear combinations from two sets of variables with maximum pairwise correlation. So far, however, this method has been used without incorporating prior information available for fMRI data. In this paper, we address this issue by proposing a new CCA method named pCCA (for projection CCA). Methods: The proposed method is obtained by projection onto a set of basis vectors that better characterize temporal information in the fMRI data set. A methodology is presented to describe the basis selection process using discrete cosine transform (DCT) basis functions. Employing DCT guides the estimated canonical variates, yielding a more computationally efficient CCA procedure. Results: The performance gain of the proposed pCCA algorithm over standard and regularized CCA is illustrated on both simulated and real fMRI datasets from resting state, block paradigm task-related and event-related experiments. The results show that pCCA can successfully extract the task as well restingstate latent components with increased specificity. Conclusion: In addition to offering a new CCA approach, when applied on fMRI data, the proposed algorithm adapts to variations of brain activity patterns and reveals the functionally connected brain regions. Significance: The proposed method can be seen as a regularized CCA method where regularization is introduced via basis expansion, which has the advantage of enforcing smoothness on canonical components.

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Fast regularized canonical correlation analysis

Cited by 39 publications

References 25 publications

Groundwater Isolation Governs Chemistry and Microbial Community Structure along Hydrologic Flowpaths

Groundwater Isolation Governs Chemistry and Microbial Community Structure along Hydrologic Flowpaths

Neurophysiological indices of audiovisual speech integration are enhanced at the phonetic level for speech in noise

A Projection CCA Method for Effective fMRI Data Analysis

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