Results 21 to 30 of about 137,290 (281)
Canonical correlation analysis for functional data [PDF]
Biometrical Letters, 2013 Summary Classical canonical correlation analysis seeks the associations between two data sets, i.e. it searches for linear combinations of the original variables having maximal correlation. Our task is to maximize this correlation, and is equivalent to solving a generalized eigenvalue problem.
Mirosław Krzyśko, Łukasz Waszak
openaire +1 more source
Brain Mapping of Behavioral Domains Using Multi-Scale Networks and Canonical Correlation Analysis [PDF]
, 2022 Simultaneous mapping of multiple behavioral domains into brain networks remains a major challenge. Here, we shed some light on this problem by employing a combination of machine learning, structural and functional brain networks at different spatial ...
Aginako, Naiara +11 more
core +1 more source
Homogeneous-Multiset-CCA-Based Brain Covariation and Contravariance Connectivity Network Modeling
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023 Brain connectivity networks based on functional magnetic resonance imaging (fMRI) have expanded our understanding of brain functions in both healthy and diseased states.
Qinrui Ling +6 more
doaj +1 more source
Generalized canonical correlation analysis for functional data
Biometrical Letters, 2020 Summary There is a growing need to analyze data sets characterized by several sets of variables observed on the same set of individuals. Such complex data structures are known as multiblock (or multiple-set) data sets.
Tomasz Górecki +2 more
openaire +1 more source
Bootstrap Methods for Canonical Correlation Analysis of Functional Data
Journal of Data Science and Intelligent Systems, 2023 The bootstrap method is a very general resampling procedure for investigating the distributional property of statistics. In this paper, we present two bootstrap methods with the aim of studying the functional canonical components for functional data. The bootstrap I method constructs the bootstrap replications by resampling from the raw data, while the
Haoyu Yu, Lihong Wang
openaire +1 more source
Improved Interpretability of Brain-Behavior CCA With Domain-Driven Dimension Reduction
Frontiers in Neuroscience, 2022 Canonical Correlation Analysis (CCA) has been widely applied to study correlations between neuroimaging data and behavioral data. Practical use of CCA typically requires dimensionality reduction with, for example, Principal Components Analysis (PCA ...
Zhangdaihong Liu +4 more
doaj +1 more source
A Gene Clustering Algorithm Based on the CCA-Hierarchical Clustering
Journal of Harbin University of Science and Technology, 2023 Aiming at the massive gene expression data brought by gene chip technology , in order to fully mine the biological information and potential biological mechanisms contained in it , this paper proposes a gene clustering algorithm based on CCA-
LIN Qianmin
doaj +1 more source
Analisis Korelasi Kanonik Permintaan Non-Fungsional
Binus Business Review, 2013 Interdependency analysis (canonical correlation analysis) intends to determine how much influence among the variables comprising two groups of variables (set variable) reciprocal between demand variables non-functional group member to request a non ...
Iskandar Putong
doaj +1 more source
SHrinkage Covariance Estimation Incorporating Prior Biological Knowledge with Applications to High-Dimensional Data [PDF]
, 2011 In ``-omic data'' analysis, information on the structure of covariates are broadly available either from public databases describing gene regulation processes and functional groups such as the Kyoto encyclopedia of genes and genomes (KEGG), or from ...
Tenenhaus, Arthur +3 more
core +1 more source
Faktor Permintaan Non Fungsional Group Member terhadap Permintaan (Seri 1)
Binus Business Review, 2010 Non-functional analysis of demand factors on demand for group members are aimed to discover whether there is significant impact of non functional variable demand potential purchasing decision.
Iskandar Putong
doaj +1 more source