Project details for SSA Toolbox

Logo SSA Toolbox 1.0

by paulbuenau - March 16, 2011, 17:47:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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The SSA Toolbox is an efficient, platform-independent, standalone implementation of the Stationary Subspace Analysis algorithm with a friendly graphical user interface and a bridge to Matlab.

Stationary Subspace Analysis (SSA) is a general purpose algorithm for the explorative analysis of non-stationary data, i.e. data whose statistical properties change over time. SSA helps to detect, characterize and visualize temporal changes in complex high-dimensional data sets.

The SSA Toolbox is written entirely in Java and is thus platform-independent. It has been tested successfuly under Windows, Linux and MacOS. The SSA Toolbox comes with a state-of-the-art native Linear Algebra library (BLAS / LAPACK) that is invoked if the operating system supports it. If not, a purely Java-based library, COLT, will be used that ensures maximum platform-independence. Data and results can imported and exported as comma-separated values (CSV) files, the fail-safe format of last resort, and through Matlab’s pro- prietory mat files, a de-facto standard in the Machine Learning community.

Changes to previous version:
  • Various bug fixes.
  • Improved GUI.
  • Added option for heuristic parameter choice.
  • Extended developer support (unit tests, documentation)
BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Platform Independent
Data Formats: Matlab, Csv
Tags: Blind Source Separation, Nonstationary Data, Stationary Subspace Analysis
Archive: download here

Other available revisons

Version Changelog Date
  • Various bugfixes.
October 24, 2011, 15:31:07
  • Added example data and Matlab script for generating synthetic data sets.

  • Improved API documentation and example for how to use the toolbox as a Java library.

  • Added a command-line interface.

  • Optimization of the non-stationary sources, in order to improve the result in the case where the correlations between s- and n-sources are not time-constant.

  • The objective function has been normalized to make its value interpretable.

  • Methodological appendices have been added to the manual.

August 12, 2011, 15:19:34
  • Better diagnostic messages in case of errors.
April 1, 2011, 11:00:57
  • Various bug fixes.
  • Improved GUI.
  • Added option for heuristic parameter choice.
  • Extended developer support (unit tests, documentation)
March 16, 2011, 17:47:23

Initial Announcement on

February 1, 2011, 22:42:03


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