DSPCA: Sparse PCA using semidefinite programming
Description
TITLE: DSPCA: Sparse PCA using semidefinite programming.
AUTHORS: Ronny Luss, Alexandre d'Aspremont, Laurent El Ghaoui
CONTENT: This package contains the numerical code used in the corresponding paper. The M-files, source code and MEX binaries for Linux, Mac OS X and Windows are available below. PathSPCA, another much simpler code for computing a full path of approximate solutions based on the results in the JMLR paper “Optimal Solutions for Sparse Principal Component Analysis.” by A. d'Aspremont, F. Bach and L. El Ghaoui is available here. This code is available in pure MATLAB and Python, is much faster, scales better and is easier to use. By default, try this one first. The results are only slighty less accurate on “natural” data and significantly worst on purely random matrices.
SOURCE CODE: DSPCA Source. Unless you are a MATLAB/MEX shogun, by default, please try PathSPCA first.
INSTRUCTIONS: DSPCA User Guide.
Release History
Version 0.5: (May 2008) Updated MATLAB/MEX binaries for intel Macs, Win32, and LINUX. Directly calls ARPACK, much faster but harder to compile on Win32.
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