97288403sparsePCA
所属分类:数值算法/人工智能
开发工具:matlab
文件大小:17KB
下载次数:11
上传日期:2018-12-10 14:01:47
上 传 者:
林12-
说明: 稀疏主成分法使主成分变得更加稀疏,以便于解释实际问题,运用逆幂法给出了求解目标函数的迭代算法取得了很好的效果.
(The sparse principal component method makes the principal component more sparse, so as to explain the actual problem. The iterative algorithm for solving the objective function is given by the inverse power method, and good results are obtained.)
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
近期下载者:
相关文件:
收藏者: