(aside image)

LineartimeNeRV: fast dimensionality reduction for information visualization

The LineartimeNeRV software package implements the linear time Neighbor Retrieval Visualizer (NeRV) dimensionality reduction algorithm recently developed by the Statistical Machine Learning and Bioinformatics group at ICS, which is a faster version of the original NeRV. If you use the algorithm, please cite the relevant papers:

LinearTimeNeRV:
Jaakko Peltonen and Konstantinos Georgatzis. Efficient Optimization for Data Visualization as an Information Retrieval Task. In MLSP 2012, the 2012 IEEE International Workshop on Machine Learning for Signal Processing, accepted for publication.

NeRV:
Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Helena Aidos, and Samuel Kaski. Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization. Journal of Machine Learning Research, 11:451-490, 2010.

Jarkko Venna and Samuel Kaski. Nonlinear Dimensionality Reduction as Information Retrieval. In Marina Meila and Xiaotong Shen, editors, Proceedings of AISTATS 2007, the 11th International Conference on Artificial Intelligence and Statistics. Omnipress, 2007. JMLR Workshop and Conference Proceedings, Volume 2: AISTATS 2007.

The software is written in ANSI/ISO C++ and licensed under the LGPL . Although developed and tested under GNU/Linux, the code should compile and run out of the box on any platform with an ANSI/ISO-compatible C++ compiler.

The current version is 0.9.0, released on 10.08.2012.

Documentation

See the README file included in the package.

Getting and installing the software

Download the tar-ball here, and extract it into an empty directory of your choosing. If you are in a UNIX-like environment and have GNU make and a recent version of g++ installed, simply typing 'make lineartimenerv' at the command line in the directory where you extracted the files should compile linearNeRV. See the included README file for instructions on using the software.

Support

This is experimental software provided as is; we welcome any comments and corrections but cannot give any guarantees about the code. If you have any comments or bug reports, please direct them to Konstantinos Georgatzis or Jaakko Peltonen.