Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework

NeurIPS 2019: Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion

A Julia package for advanced Matrix Diagonalization algorithms (PCA, Whitening, MCA, gMCA, CCA, gCCA, CSP, CSTP, AJD, mAJD)

Deep Multiset Canonical Correlation Analysis - An extension of CCA to multiple datasets

A basic demonstration how to use Python, MNE, and PyTorch to analyze EEG signal.

MoMA: Modern Multivariate Analysis in R

Implementation of Fast ml-CCA from the ICCV-2015 work "Multi-Label Cross-Modal Retrieval"

This repository includes useful MATLAB codes for the detection of SSVEP in EEG signals using spatial filters, frequency recognition algorithms, and machine-learning methods.

Several examples of multivariate techniques implemented in R, Python, and SAS. Multivariate concrete dataset retrieved from https://archive.ics.uci.edu/ml/datasets/Concrete+Slump+Test. Credit to Professor I-Cheng Yeh.

Implementations of gradKCCA

Efficient sparse matrix implementation for various "Principal Component Analysis"

Data mining based approach to study the effect of caffeinated coffee on SSVEP brain signals. https://doi.org/10.1016/j.compbiomed.2019.103526

ISC method for M/EEG data

Case Study in ranking U.S. cities based on a single linear combination of rating variables. Dimensionality techniques used in the analysis are Principal Component Analysis (PCA), Factor Analysis (FA), Canonical Correlation Analysis (CCA)

Time-dependent Canonical Correlation Analysis

Sparse canonical correlation analysis

Deep Canonical Correlation Analysis with Python

TreeCorTreat

Unsupervised Learning

Tensor-based Multiple Canonical Correlation Analysis