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- Description:
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
- Supported Operating Systems: Platform Independent
- Data Formats: Matlab, Csv
- Tags: Blind Source Separation, Nonstationary Data, Stationary Subspace Analysis
- Archive: download here
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