Software

MCCA

Package

MCCA Computes the multilinear common component analysis (MCCA) based on Kronecker products of mode-wise covariance matrices. This package can perform the dimensionality reduction by estimating the basis which represent the intrinsic common structure of multiple tensor datasets.

Reference

  1. Yoshikawa, K. and Kawano, S. (2020) "Multilinear Common Component Analysis via Kronecker Product Representation". arXiv:2009.02695 [URL].

RVSManOpt

Package

RVSManOpt Computes the sparse reduced-rank factor regression based on manifold optimization. This package can perform estimating the rank of the coefficient matrix, selecting the number of explanatory variables which composes factors included in the regression, and selecting the number of the factors are relevant with the response variables.

Reference

Yoshikawa, K. and Kawano, S. (2019) "Sparse reduced-rank regression for simultaneous rank and variable selection via manifold optimization" arXiv:1910.05083 [URL].