Results 1 to 10 of about 462,742 (146)

Fair Canonical Correlation Analysis. [PDF]

Adv Neural Inf Process Syst, 2023
Accepted for publication at NeurIPS 2023, 31 Pages, 14 ...
Zhou Z, Tarzanagh DA, Hou B, Tong B, Xu J, Feng Y, Long Q, Shen L.   +7 more
europepmc   +4 more sources

Permutation inference for canonical correlation analysis [PDF]

NeuroImage, 2020
Canonical correlation analysis (CCA) has become a key tool for population neuroimaging, allowing investigation of associations between many imaging and non-imaging measurements.
Anderson M. Winkler, Olivier Renaud, Stephen M. Smith, Thomas E. Nichols   +3 more
doaj   +4 more sources

Longitudinal Canonical Correlation Analysis. [PDF]

J R Stat Soc Ser C Appl Stat, 2023
AbstractThis paper considers canonical correlation analysis for two longitudinal variables that are possibly sampled at different time resolutions with irregular grids. We modelled trajectories of the multivariate variables using random effects and found the most correlated sets of linear combinations in the latent space.
Lee S, Choi J, Fang Z, Bowman FD, Alzheimer’s Disease Neuroimaging Initiative.   +4 more
europepmc   +5 more sources

Robust Sparse Canonical Correlation Analysis [PDF]

SSRN Electronic Journal, 2014
Canonical correlation analysis (CCA) is a multivariate statistical method which describes the associations between two sets of variables. The objective is to find linear combinations of the variables in each data set having maximal correlation.
Croux, Christophe, Wilms, Ines
core   +6 more sources

Sufficient Canonical Correlation Analysis [PDF]

IEEE Transactions on Image Processing, 2016
Canonical correlation analysis (CCA) is an effective way to find two appropriate subspaces in which Pearson’s correlation coefficients are maximized between projected random vectors.
Ding, X, Guo, Y, Liu, C, Xue, J-H
core   +5 more sources

An Improved Canonical Correlation Analysis for EEG Inter-Band Correlation Extraction [PDF]

Bioengineering, 2023
(1) Background: Emotion recognition based on EEG signals is a rapidly growing and promising research field in affective computing. However, traditional methods have focused on single-channel features that reflect time-domain or frequency-domain ...
Zishan Wang, Ruqiang Huang, Ye Yan, Zhiguo Luo, Shaokai Zhao, Bei Wang, Jing Jin, Liang Xie, Erwei Yin   +8 more
doaj   +2 more sources

Multimodal Brain Growth Patterns: Insights from Canonical Correlation Analysis and Deep Canonical Correlation Analysis with Auto-Encoder [PDF]

Information
Today’s advancements in neuroimaging have been pivotal in enhancing our understanding of brain development and function using various MRI techniques.
Ram Sapkota, Bishal Thapaliya, Bhaskar Ray, Pranav Suresh, Jingyu Liu   +4 more
doaj   +2 more sources

Canonical correlation analysis for multi-omics: Application to cross-cohort analysis. [PDF]

PLoS Genetics, 2023
Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest.
Min-Zhi Jiang, François Aguet, Kristin Ardlie, Jiawen Chen, Elaine Cornell, Dan Cruz, Peter Durda, Stacey B Gabriel, Robert E Gerszten, Xiuqing Guo, Craig W Johnson, Silva Kasela, Leslie A Lange, Tuuli Lappalainen, Yongmei Liu, Alex P Reiner, Josh Smith, Tamar Sofer, Kent D Taylor, Russell P Tracy, David J VanDenBerg, James G Wilson, Stephen S Rich, Jerome I Rotter, Michael I Love, Laura M Raffield, Yun Li, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Analysis Working Group   +27 more
doaj   +2 more sources

Structure-adaptive canonical correlation analysis for microbiome multi-omics data [PDF]

Frontiers in Genetics
Sparse canonical correlation analysis (sCCA) has been a useful approach for integrating different high-dimensional datasets by finding a subset of correlated features that explain the most correlation in the data.
Linsui Deng, Yanlin Tang, Xianyang Zhang, Jun Chen   +3 more
doaj   +2 more sources

Chunk Incremental Canonical Correlation Analysis [PDF]

Jisuanji kexue yu tansuo, 2022
For the large-scale dynamic data stream, incremental learning is an effective and efficient technique and is widely used in machine learning. Incremental dimensionality reduction algorithms have been proposed by many scholars.
PAN Yu, CHEN Xiaohong, LI Shunming, LI Jiyong
doaj   +1 more source