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Comparing the stability and reproducibility of brain-behavior relationships found using canonical correlation analysis and partial least squares within the ABCD sample. [PDF]

Netw Neurosci
Nakua H, Yu JC, Abdi H, Hawco C, Voineskos A, Hill S, Lai MC, Wheeler AL, McIntosh AR, Ameis SH.   +9 more
europepmc   +1 more source

Influence in Canonical Correlation Analysis

Psychometrika, 1992
The perturbation theory of the generalized eigenproblem is used to derive influence functions of each squared canonical correlation coefficient and the corresponding canonical vector pair. Three sample versions of these functions are described and some properties are noted.
Mario Romanazzi
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A Survey on Canonical Correlation Analysis

IEEE Transactions on Knowledge and Data Engineering, 2021
In recent years, the advances in data collection and statistical analysis promotes canonical correlation analysis (CCA) available for more advanced research. CCA is the main technique for two-set data dimensionality reduction such that the correlation between the pairwise variables in the common subspace is mutually maximized.
Xinghao Yang, Weifeng Liu 0001, Wei Liu 0007, Dacheng Tao   +3 more
openaire   +2 more sources

Regularized Generalized Canonical Correlation Analysis

Psychometrika, 2011
generalized canonical correlation analysis, multi-block data analysis, PLS path modeling, regularized canonical correlation analysis,
Arthur Tenenhaus, Michel Tenenhaus
exaly   +2 more sources

Canonical random correlation analysis

Proceedings of the 2010 ACM Symposium on Applied Computing, 2010
Canonical correlation analysis (CCA) is one of the most well-known methods to extract features from multi-view data and has attracted much attention in recent years. However, classical CCA is unsupervised and does not take class label information into account.
Jianchun Zhang, Daoqiang Zhang
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Parametric Canonical Correlation Analysis

2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), 2019
Generally, suppose a wave is a linear combination of multiple basis(Not necessarily a sine or cosine waves, it could also be a wavelet, etc.), different types of waves may be similar on some basis, but vary greatly on a certain basis. To address this problem, we introduce a PCCA-based feature extraction method that extends canonical correlation ...
Shangyu Chen, Shuo Wang 0012, Richard O. Sinnott   +2 more
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Generalised Canonical Correlation Analysis

2000
Canonical Correlation Analysis [3] is used when we have two data sets which we believe have some underlying correlation. In this paper, we derive a new family of neural methods for finding the canonical correlation directions by solving a generalized eigenvalue problem. Based on the differential equation for the generalized eigenvalue problem, a family
Zhenkun Gou, Colin Fyfe
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KERNEL AND NONLINEAR CANONICAL CORRELATION ANALYSIS

International Journal of Neural Systems, 2000
We review a neural implementation of the statistical technique of Canonical Correlation Analysis (CCA) and extend it to nonlinear CCA. We then derive the method of kernel-based CCA and compare these two methods on real and artificial data sets before using both on the Blind Separation of Sources.
Pei Ling Lai, Colin Fyfe
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