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Neurons as Canonical Correlation Analyzers [PDF]
Frontiers in Computational Neuroscience, 2020 Normative models of neural computation offer simplified yet lucid mathematical descriptions of murky biological phenomena. Previously, online Principal Component Analysis (PCA) was used to model a network of single-compartment neurons accounting for ...
Cengiz Pehlevan +4 more
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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
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Canonical Concordance Correlation Analysis
Mathematics, 2022 A multivariate technique named Canonical Concordance Correlation Analysis (CCCA) is introduced. In contrast to the classical Canonical Correlation Analysis (CCA) which is based on maximization of the Pearson’s correlation coefficient between the linear ...
Stan Lipovetsky
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Canonical Correlations and Nonlinear Dependencies [PDF]
Symmetry, 2021 Canonical correlation analysis (CCA) is the default method for investigating the linear dependence structure between two random vectors, but it might not detect nonlinear dependencies. This paper models the nonlinear dependencies between two random vectors by the perturbed independence distribution, a multivariate semiparametric model where CCA ...
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An Improved Canonical Correlation Analysis for EEG Inter-Band Correlation Extraction
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 +8 more
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Incremental Canonical Correlation Analysis
Applied Sciences, 2020 Canonical correlation analysis (CCA) is a kind of a simple yet effective multiview feature learning technique. In general, it learns separate subspaces for two views by maximizing their correlations.
Hongmin Zhao, Dongting Sun, Zhigang Luo
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Canonical Correlation Analysis to Biomass CHONS Prediction
Chemical Engineering Transactions, 2023 Fermentation biomasses can be defined as a complex mixture of different natural components and microbes, having biodegradable and organic waste as the primary source. Its correct characterization is crucial to have proper processing in fermentative units.
Federico Moretta +4 more
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Supervised Canonical Correlation Analysis Based on Deep Learning [PDF]
Jisuanji gongcheng, 2022 Canonical Correlation Analysis (CCA) is a multivariate statistical method, which uses the correlation between comprehensive variable pairs to reflect the overall correlation between two groups of indicators.The traditional CCA method can not effectively ...
ZHANG Heng, CHEN Xiaohong, LAN Yuxiang, LI Shunming
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Longitudinal canonical correlation analysis
Journal of the Royal Statistical Society Series C: Applied Statistics, 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.
Seonjoo Lee +3 more
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A Tutorial on Canonical Correlation Methods [PDF]
ACM Computing Surveys, 2017 Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables. Since its proposition, canonical correlation analysis has, for instance, been extended to extract relations between two sets of variables when the sample size is insufficient in relation to the data dimensionality, when the ...
Monteiro, João M +6 more
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