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

Niya Wang, Fan Meng, Li Chen, Subha Madhavan, Robert Clarke, Eric P. Hoffman, Jianhua Xuan, Yue Wang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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 separatingmixed 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.

Original languageEnglish (US)
Pages (from-to)2899-2903
Number of pages5
JournalJournal of Machine Learning Research
Volume14
StatePublished - Sep 2013
Externally publishedYes

Keywords

  • Affinity propagation clustering
  • Blind source separation
  • Compartment modeling
  • Convex analysis of mixtures
  • Information-based model selection

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