Description:

The CAM R-Java software provides a noval way to solve blind source separation problem. It consists of three sub-algorithms, CAM-CM, CAM-nICA and CAM-nWCA based on the assumption of nonnegative sources. CAM has been successfully used in solving real world BSS problems, such as mixing aerial images dissection, longitudinal DCE-MRI deconvolution, etc.

CAM assumes that sources contain sufficient number of well-grounded points (WGPs) at which signals are highly expressed in one source relative to each of the remaining sources, the goal is to estimate the column vectors of mixing matrix by identifying WGPs located at the corners of mixture observation scatter simplex and subsequently recover the hidden source signals. Based on a geometrical latent variable model, CAM learns the mixing matrix by identifying the lateral edges of convex data scatter plot. The algorithm is supported theoretically by a well-grounded mathematical framework.

All the core functions are realized in R, and the software provides a Java Graphic-User-Interface that is easy to use. Four datasets are provided to help uses test and understand how this software works.

This software also provides standard algorithms for NMF, fastICA, and factor analysis. Users can easily plug in their own algorithms by using the plug-in mechanism we provide in the software.

Changes to previous version:

In this version, we fix the problem of not working under newest R version R-3.0.

BibTeX Entry: Download

Supported Operating Systems: Windows, Mac Os X, Ubuntu

Data Formats: Txt, Csv, Rdata, Mat

Tags: Nonnegative Matrix Factorization, Blind Source Separation, Affinity Propagation Clustering, Compartment Modeling, Convex Analysis Of Mixtures, Information Based Model Selection, Factor Analysis, Indep

Archive: download here

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