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

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

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

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.

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

MoMA: Modern Multivariate Analysis in R

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"

ISC method for M/EEG data

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

Deep Canonical Correlation Analysis with Python

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)

TreeCorTreat

Time-dependent Canonical Correlation Analysis

Sparse canonical correlation analysis

Unsupervised Learning

This repository contains materials associated to the course "Multivariate Analysis" taught at the Faculty of Mathematics and Statistics (FME), UPC under the MESIO-UPC-UB Interuniversity Program under the instructors "Ferran Revertar", "Miguel Salicru" and "Jan Graffelman"