CAM Javahttp://mloss.orgUpdates and additions to CAM JavaenMon, 14 Oct 2013 22:46:03 -0000CAM Java 3.1<html><p>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. </p> <p>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. </p> <p>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. </p> <p>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. </p></html>Niya Wang, Fan MengMon, 14 Oct 2013 22:46:03 -0000 matrix factorizationblind source separationaffinity propagation clusteringcompartment modelingconvex analysis of mixturesinformation based model selectionfactor analysisindep