sparsePCA

所属分类:图形图像处理
开发工具:matlab
文件大小:18KB
下载次数:94
上传日期:2013-08-30 12:22:47
上 传 者hejinrong
说明:  稀疏主成分分析算法,用于特征提取,维数约简。
(Sparse Principle Component Analysis for feature extraction)

文件列表:
InvPow_SparsePCA_V1_0 (0, 2012-01-07)
InvPow_SparsePCA_V1_0\invPow.m (2196, 2012-01-06)
InvPow_SparsePCA_V1_0\LICENSE (35147, 2010-12-17)
InvPow_SparsePCA_V1_0\sparsePCA.m (12121, 2012-01-07)

SPARSE PCA VIA NONLINEAR INVERSE POWER METHOD This archive contains a Matlab implementation of Sparse PCA using the inverse power method for nonlinear eigenproblems as described in the paper M. Hein and T. Buehler An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA In Advances in Neural Information Processing Systems 23 (NIPS 2010). (Extended version available online at http://arxiv.org/abs/1012.0774) Current version: V1.0 SHORT DOCUMENTATION Usage: [cards,vars,Z]= sparsePCA(X,card); [cards,vars,Z]= sparsePCA(X,card_min,card_max); [cards,vars,Z]= sparsePCA(X,card_min,card_max,numRuns); [cards,vars,Z]= sparsePCA(X,card_min,card_max,numRuns,verbosity); X : data matrix (num x dim) card : desired number of non-sparse components of output (cardinality) card_min,card_max : computes all vectors with cardinality values in intervall [card_min,card_max] (default: card_min=card_max) numRuns : number of runs of inverse power method with random initialization (default: 10) verbosity [0-2]: determines how much information is displayed (default: 1) cards : the cardinalities (number of nonzero components) of the returned vectors vars : the corresponding vectors Z : the sparse principal components LICENSE This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . If you use this code for your publication, please include a reference to the paper "An inverse power method for nonlinear eigenproblems with applications in 1-spectral clustering and sparse PCA". CONTACT (C)2010-2011 Thomas Buehler and Matthias Hein tb,hein@cs.uni-saarland.de Machine Learning Group, Saarland University, Germany http://www.ml.uni-saarland.de

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