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

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

Implementations of gradKCCA

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

Deep Canonical Correlation Analysis with Python

MoMA: Modern Multivariate Analysis in R

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

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

Generalized Canonical Correlation Analysis - python 3 version

Cross-validation-based maximal associations

Time-dependent Canonical Correlation Analysis

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

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.

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"

An implementation of Deep Canonical Correlation Analysis (DCCA or Deep CCA) in Keras with tfv2 backend.

Efficient sparse matrix implementation for various "Principal Component Analysis"

Find models of interaction

Tensor-based Multiple Canonical Correlation Analysis

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)

MATLAB and R Code for sparse GCA