Project details for CAM Java

Logo CAM Java 2.0

by wangny - April 11, 2013, 18:21:12 CET [ BibTeX Download ]

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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.

In the latest version, three classic BSS algorithms are combined into the software, and a simple plugin mechanism is designed to allow users adding their own algorithms into it.

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

Changes to previous version:
  1. Three classic BSS algorithms - NMF, fastICA and Factor Analysis - are combined into the software. Users can readily call the three functions from Java GUI
  2. A simple plug-in mechanism is added. Users can add their own BSS algorithm into the software by following the Software Plugin Adding Guide and User Manual
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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