https://github.com/andrewssobral/tdalab

A MATLAB Toolbox for High-order Tensor Data Decompositions and Analysis
https://github.com/andrewssobral/tdalab

multidimensional-arrays tensor-decomposition tensor-factorization tensor-toolbox

Last synced: 3 months ago
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A MATLAB Toolbox for High-order Tensor Data Decompositions and Analysis

• Host: GitHub
• URL: https://github.com/andrewssobral/tdalab
• Owner: andrewssobral
• License: other
• Created: 2018-01-30T20:10:40.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-01-30T20:45:15.000Z (over 7 years ago)
• Last Synced: 2025-03-24T07:57:01.346Z (3 months ago)
• Topics: multidimensional-arrays, tensor-decomposition, tensor-factorization, tensor-toolbox
• Language: Matlab
• Homepage: http://bsp.brain.riken.jp/TDALAB/
• Size: 3.17 MB
• Stars: 20
• Watchers: 1
• Forks: 8
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
TDALAB

======

### Laboratory for Tensor Decomposition and Analysis

_by Guoxu Zhou, Andrzej Cichocki_  

2012 Cichocki Laboratory for Advanced Brain Signal Processing

[Download TDALAB manual](http://bsp.brain.riken.jp/~zhougx/tdalab/tdalab_guide.pdf)  

Current version 1.1, released May 1st, 2013.  

For any support, request and bug reports contact [guoxu.zhou(at)riken.jp](http://bsp.brain.riken.jp/~zhougx/).

* * *

Datafiles for testing are available in [TDALAB benchmark](http://bsp.brain.riken.jp/~zhougx/tdalab/TDALAB_benchmark.zip) \[~30MB\].

Tensor Toolbox (TDALAB) provides fundamental data structures and functions for tensor data processing. TDALAB attempts to provide an easy to use, user-friendly toolbox for experimentation and application of tensor decomposition and analysis.

![](http://bsp.brain.riken.jp/TDALAB/TDALAB.jpg)

#### TDALAB highlights and features

*   friendly graphical user interface (GUI) for tensor decompositions, enabling easy selection of decomposition model, algorithm, and parameters

*   platform for comparison and evaluation of a large number of state-of-the-art tensor decomposition algorithms, and provides friendly GUI to access the widely used functions included in [N-way Toolbox](http://www.models.life.ku.dk/nwaytoolbox) and [Tensor Toolbox](http://www.sandia.gov/~tgkolda/TensorToolbox/), and some of the latest developments in tensor decompositions.

*   implementation of constrained tensor decomposition by incorporating standard 2D Penalized Matrix Factorization (PMF) methods in order to impose diversity/constraints on components (columns of factor matrices), such as orthogonality, statistical independence, sparsity, nonnegativity, etc (as in [Multilinear Blind Source Separation (MBSS)](http://www.degruyter.com/view/j/bpasts.2012.60.issue-3/v10175-012-0051-4/v10175-012-0051-4.xml)).

*   implementation of 2D constrained matrix factorization (also referred to as 2D Blind Source Separation (BSS))

*   multiple visualization approaches for tensor objects are provided, users can explore the components and their connections

Link to the [Laboratory homepage](http://www.bsp.brain.riken.jp/)