44,442 Hits in 4.1 sec

Genetic differentiation in life history traits and thermal stress performance across a heterogeneous dune landscape in Arabidopsis lyrata

Guillaume Wos, Yvonne Willi
2018 Annals of Botany  
Offspring plants were raised in climate chambers and exposed to one of three temperature treatments: regular occurrence of frost, heat, or constant control conditions.  ...  . • Key Results The strongest trait-environment association was between a fast switch to sexual reproduction and weaker growth under frost, and growing in the open, away from trees.  ...  Canonical correlation analysis.  ... 
doi:10.1093/aob/mcy090 pmid:29846507 fatcat:4mhao3a47ff3rcg64ye2qprajy

2nd Special issue on matrix computations and statistics

Jesse L. Barlow, Haesun Park, Patrick J.F. Groenen, Hongyuan Zha
2006 Computational Statistics & Data Analysis  
, high-speed smoothing for large grids, a theoretical and practical comparison of canonical correlation analysis and Procrustes analysis, a generalization of constrained correspondence analysis, a new  ...  correlations, several cluster analysis algorithms, and the solution of total least-squares problems.  ...  Canonical correlations were among the themes of three papers. Gardner et al. (2004) synthesize canonical variate analysis, Procrustes analysis, and generalized canonical correlation.  ... 
doi:10.1016/j.csda.2004.08.003 fatcat:4bty4gk6g5fi3hz3fraqzm4evu

Large scale canonical correlation analysis with iterative least squares [article]

Yichao Lu, Dean P. Foster
2014 arXiv   pre-print
Canonical Correlation Analysis (CCA) is a widely used statistical tool with both well established theory and favorable performance for a wide range of machine learning problems.  ...  In this paper we introduce L-CCA, a iterative algorithm which can compute CCA fast on huge sparse datasets.  ...  Background: Canonical Correlation Analysis Definition Canonical Correlation Analysis (CCA) can be defined in many different ways.  ... 
arXiv:1407.4508v2 fatcat:7hnv6rb4uffmjasqovgahng4ty

Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging

Maria J. Rosa, Mitul A. Mehta, Emilio M. Pich, Celine Risterucci, Fernando Zelaya, Antje A. T. S. Reinders, Steve C. R. Williams, Paola Dazzan, Orla M. Doyle, Andre F. Marquand
2015 Frontiers in Neuroscience  
One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA).  ...  A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution.  ...  ., subjects) is canonical correlation analysis (CCA) (Hotelling, 1936) .  ... 
doi:10.3389/fnins.2015.00366 pmid:26528117 pmcid:PMC4603249 fatcat:rcjpfwma7nb2lkhll5xlp6khc4

Fast Multi-Task SCCA Learning with Feature Selection for Multi-Modal Brain Imaging Genetics

Lei Du, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Lei Guo, Andrew J. Saykin, Li Shen
2018 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analyses.  ...  Compared with conventional SCCA methods, MTSCCA obtains improved performance regarding both correlation coefficients and canonical weights patterns.  ...  As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.  ... 
doi:10.1109/bibm.2018.8621298 pmid:30881731 pmcid:PMC6413500 fatcat:jr6olojszfglnoznvl6q4d7dwa

Cross-domain recommender system using Generalized Canonical Correlation Analysis [article]

Seyed Mohammad Hashemi, Mohammad Rahmati
2019 arXiv   pre-print
In this representation we proposed an iterative method which applied MAX-VAR generalized canonical correlation analysis (GCCA) on users latent factors learned from matrix factorization on each domain.  ...  Also, to improve the capability of GCCA to learn latent factors for new users, we propose generalized canonical correlation analysis by inverse sum of selection matrices (GCCA-ISSM) approach, which provides  ...  canonical correlation basis vectors. 3.2 Generalized canonical correlation analysis Generalized canonical correlation analysis (GCCA) is the extended version of CCA that could be applied on more than  ... 
arXiv:1909.12746v1 fatcat:lbviw5dedjfwbj3jygxicfx73a

SEEDCCA: An Integrated R-Package for Canonical Correlation Analysis and Partial Least Squares

Bo-Young Kim, Yunju Im, Jae,Keun Yoo
2021 The R Journal  
Canonical correlation analysis (CCA) has a long history as an explanatory statistical method in high-dimensional data analysis and has been successfully applied in many scientific fields such as chemometrics  ...  Also, the seeded CCA application results are compared with the regularized CCA in the existing R package.  ...  correlation analysis (CCA; Hotelling (1936) ).  ... 
doi:10.32614/rj-2021-026 fatcat:ir737cl5dzexdieuapqpkzkroa

Table of contents - Volume 1

2013 2013 12th International Conference on Machine Learning and Applications  
Sparse Canonical Correlation Analysis .......................................129 Blood Glucose Level Prediction Using Physiological Models and Support Vector Regression ...............................  ...  ......................................123 Simone Filice, Danilo Croce, Roberto Basili, and Fabio Massimo Zanzotto Machine Learning in Bioinformatics and Computational Biology I eQTL Mapping Study via Regularized  ... 
doi:10.1109/icmla.2013.4 fatcat:ehbhpco7ijfo3cfk6eptx55v4m

Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis

Xiaofeng Zhu, Heung-Il Suk, Seong-Whan Lee, Dinggang Shen
2015 Brain Imaging and Behavior  
correlation analysis.  ...  We then perform the sparse multi-task learning for discriminative feature selection by using the canonical features as regressors and penalizing a loss function with a canonical regularizer.  ...  Concretely, the proposed canonical regularizer enforces the highly correlated canonical representations of modalities, i.e., large canonical correlation coefficients, to be selected; while the merely or  ... 
doi:10.1007/s11682-015-9430-4 pmid:26254746 pmcid:PMC4747862 fatcat:ce5aejjgpfhahc5gmf7ouvoxne

Nonlinear measures of association with kernel canonical correlation analysis and applications

Su-Yun Huang, Mei-Hsien Lee, Chuhsing Kate Hsiao
2009 Journal of Statistical Planning and Inference  
The classical canonical correlation analysis (LCCA) can characterize, but also is limited to, linear association.  ...  This nonlinear kernel canonical correlation analysis (KCCA) can also be applied to the nonlinear discriminant analysis. Implementation issues are discussed.  ...  Canonical correlation analysis: linear and kernel Review of linear canonical correlation analysis Suppose the random vector X of q components has a probability measure P on X ⊂ R q .  ... 
doi:10.1016/j.jspi.2008.10.011 fatcat:n7fbvchp25dopneocgthyuqge4

Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome

Melissa J Morine, Jolene McMonagle, Sinead Toomey, Clare M Reynolds, Aidan P Moloney, Isobel C Gormley, Peadar Ó Gaora, Helen M Roche
2010 BMC Bioinformatics  
Furthermore, we apply canonical correlation analysis and Fisher's exact test, using plasma marker data with known clinical relevance to aid identification of the most important gene and pathway changes  ...  In such cases where transcriptomic response to treatment is exceptionally large, canonical correlation analysis in conjunction with Fisher's exact test highlights the subset of pathways showing strongest  ...  Regularized canonical correlation analysis identifies dietsensitive genes and pathways showing strong association with metabolic health Canonical correlation analysis (CCA) is a multivariate statistical  ... 
doi:10.1186/1471-2105-11-499 pmid:20929581 pmcid:PMC3098081 fatcat:3e5wbc4errcrhhrvpbaswsyx6q

Randomized Nonlinear Component Analysis [article]

David Lopez-Paz, Suvrit Sra, Alex Smola, Zoubin Ghahramani, Bernhard Schölkopf
2014 arXiv   pre-print
Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics.  ...  In this paper we leverage randomness to design scalable new variants of nonlinear PCA and CCA; our ideas extend to key multivariate analysis tools such as spectral clustering or LDA.  ...  For CCA, common extensions are Kernel Canonical Correlation Analysis or KCCA (Lai & Fyfe, 2000; Bach & Jordan, 2002) and Deep Canonical Correlation Analysis (Andrew et al., 2013) .  ... 
arXiv:1402.0119v2 fatcat:uakkcev4pvgjje7hbq4wfekv6y

What lexical decision and naming tell us about reading

Leonard Katz, Larry Brancazio, Julia Irwin, Stephen Katz, James Magnuson, D. H. Whalen
2011 Reading and writing  
In each canonical correlation, there were the same predictor variables: NAM regular RT, NAM irregular RT, LD regular RT, LD irregular RT, LD pseudoword RT, and as a control variable for basic response  ...  Canonical correlation can be viewed as an extension of multivariate analysis of variance for the case in which both sides of the equation contain continuously scaled variables.  ... 
doi:10.1007/s11145-011-9316-9 pmid:22754142 pmcid:PMC3383646 fatcat:5vyiaxuevba2lmpubjxmtzzeme

Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and fisher discriminant analysis

Ping Wu, Yichao Liu, Riccardo M.G. Ferrari, Jan‐Willem van Wingerden
2021 IET Renewable Power Generation  
In this paper, a novel data-driven fault diagnosis method using regularized dynamic canonical correlation analysis (RDCCA) and Fisher discriminant analysis (FDA) is proposed for FOWTs.  ...  Specifically, to overcome the collinearity problem that exists in measured process data, dynamic canonical correlation analysis with a regularization scheme, is developed to exploit the relationship between  ...  canonical correlation analysis.  ... 
doi:10.1049/rpg2.12319 fatcat:7tnfwxgftjhm5o4z7spvfvgeey

Structured sparse deconvolution for paradigm free mapping of functional MRI data

Cesar Caballero Gaudes, Fikret Isik Karahanoglu, Francois Lazeyras, Dimitri Van De Ville
2012 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)  
Such deconvolution approach can take advantage of modern sparsity-promoting regularization.  ...  Specifically, we extend the haemodynamic dictionary with the informed basis set (i.e., canonical HRF, and its temporal and dispersion derivatives) and we deploy state-of-the art structured sparsity functionals  ...  In case of correlated noise, morphological component analysis approaches for paradigm free mapping can be easily incorporated in our formulation [4] .  ... 
doi:10.1109/isbi.2012.6235549 dblp:conf/isbi/GaudesKLV12 fatcat:zgrhaqxj6fhlbjoyfd5oro4mbe
« Previous Showing results 1 — 15 out of 44,442 results