The CAM software for nonnegative blind source separation in R-Java

Authors:
Niya Wang;Fan Meng;Li Chen;Subha Madhavan;Robert Clarke;Eric P. Hoffman;Jianhua Xuan;Yue Wang
Affiliations:
Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA;Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA;National Cancer Institute, National Institutes of Health, Gaithersburg, MD;Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC;Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC;Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC;Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA;Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA
Venue:
The Journal of Machine Learning Research
Year:
2013

Citing 6
Cited 0

Blind separation of positive sources by globally convergent gradient search

Neural Computation
Nonnegative Least-Correlated Component Analysis for Separation of Dependent Sources by Volume Maximization

IEEE Transactions on Pattern Analysis and Machine Intelligence
CAM-CM

Bioinformatics
A Convex Analysis Framework for Blind Separation of Non-Negative Sources

IEEE Transactions on Signal Processing - Part II
From blind signal extraction to blind instantaneous signal separation: criteria, algorithms, and stability

IEEE Transactions on Neural Networks
Sparse and unique nonnegative matrix factorization through data preprocessing

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe a R-Java CAM (convex analysis of mixtures) package that provides comprehensive analytic functions and a graphic user interface (GUI) for blindly separating mixed nonnegative sources. This open-source multiplatform software implements recent and classic algorithms in the literature including Chan et al. (2008), Wang et al. (2010), Chen et al. (2011a) and Chen et al. (2011b). The CAM package offers several attractive features: (1) instead of using proprietary MATLAB, its analytic functions are written in R, which makes the codes more portable and easier to modify; (2) besides producing and plotting results in R, it also provides a Java GUI for automatic progress update and convenient visual monitoring; (3) multi-thread interactions between the R and Java modules are driven and integrated by a Java GUI, assuring that the whole CAM software runs responsively; (4) the package offers a simple mechanism to allow others to plug-in additional R-functions.