Canonical Correlation Analysis

By Rodrigo Malacarne. The SAGE Encyclopedia of Research Design, 2022

440 citations recorded by the source corpus.

The aim of canonical correlation analysis is to find the best linear combination between two multivariate datasets that maximizes the correlation coefficient between them. This is particularly useful to determine the relationship between criterion measures and the set of their explanatory factors. This technique involves, first, the reduction of the dimensions of the two multivariate datasets by projection, and second, the calculation of the relationship (measured by the correlation coefficient) between the two projections of the datasets.

Subfields: Analytical Chemistry

View Paper: https://www.semanticscholar.org/paper/fd79f238c0eb2b187a52209b03078ecff7a12541

How researchers see this paper

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In Analytical Chemistry

1. Yu Ding
2. Tengfei Song
3. Zhen Cui
4. Yuan Zong
5. Hung-yi Lee
6. Lei Qi
7. Wei-Tsung Kao
8. Lincheng Li

Citations in the corpus

• Utilizing Self-supervised Representations for MOS Prediction (2021, methodology): “We extended the analysis by canonical correlation analysis [24] (CCA) on four pre-trained models: wav2vec 2.”
• A Bi-Hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition (2021, methodology): “For comparison purpose, we use the linear support vector machine (SVM) [37], canonical correlation analysis (CCA) [39], group sparse canonical correlation analysis [11], deep believe network [13], graph regularization sparse linear regression [12], graph convolutional neural network (GCNN) [40] and…”
• Flow-guided One-shot Talking Face Generation with a High-resolution Audio-visual Dataset (2021, methodology): “In the evaluation of p and p, we employ SSIM and Canonical Correlation Analysis(CCA)[40] as metrics respectively.”
• A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition (2019, methodology): “To validate the classification superiority of BiHDM, we also conduct the same experiments using another 12 methods, including linear support vector machine (SVM) [29], random forest (RF) [30], canonical correlation analysis (CCA) [31], GSCCA [10], DBN [12], GRSLR [24], graph convolutional neural net”