mRMR_0.9_compiled
所属分类:matlab编程
开发工具:matlab
文件大小:481KB
下载次数:129
上传日期:2009-02-05 10:21:24
上 传 者:
reallycsc
说明: mRMR(min-redundancy max-relevance)的matlab程序
(matlab program of mRMR(min-redundancy max-relevance))
文件列表:
mRMR_0.9_compiled\mi_0.9\condentropy.m (1034, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\condmutualinfo.m (1237, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\demo_mi.m (724, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\entropy.m (761, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\estcondentropy.dll (40960, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\estcondentropy.mexglx (125024, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\estcondentropy.mexmac (54608, 2007-03-23)
mRMR_0.9_compiled\mi_0.9\estentropy.dll (40960, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\estentropy.mexglx (7255, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\estentropy.mexmac (53908, 2007-03-23)
mRMR_0.9_compiled\mi_0.9\estjointentropy.dll (40960, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\estjointentropy.mexglx (124769, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\estjointentropy.mexmac (54608, 2007-03-23)
mRMR_0.9_compiled\mi_0.9\estmutualinfo.dll (40960, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\estmutualinfo.mexglx (125610, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\estmutualinfo.mexmac (54644, 2007-03-23)
mRMR_0.9_compiled\mi_0.9\estpa.dll (40960, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\estpa.mexglx (127488, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\estpa.mexmac (59216, 2007-03-23)
mRMR_0.9_compiled\mi_0.9\estpab.dll (40960, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\estpab.mexglx (128216, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\estpab.mexmac (59180, 2007-03-23)
mRMR_0.9_compiled\mi_0.9\findjointstateab.dll (40960, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\findjointstateab.mexglx (129114, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\findjointstateab.mexmac (59260, 2007-03-23)
mRMR_0.9_compiled\mi_0.9\jointentropy.m (924, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\makeosmex.m (599, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\mergemultivariables.m (1099, 2007-01-29)
mRMR_0.9_compiled\mi_0.9\mutualinfo.m (604, 2007-01-29)
mRMR_0.9_compiled\mrmr_mibase_d.m (363, 2007-01-29)
mRMR_0.9_compiled\mrmr_mid_d.m (1277, 2007-01-29)
mRMR_0.9_compiled\mrmr_miq_d.m (1351, 2007-01-29)
mRMR_0.9_compiled\mi_0.9 (0, 2008-11-26)
mRMR_0.9_compiled (0, 2008-11-26)
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To run the mRMR variable/feature selection for discrete
variables, you need to add the path of mi_0.9 in your
Matlab path. Then simply run mrmr_mid_d.m or mrmr_miq_d.m
for your data.
If you have continuous data as input, you need to discretize
the data first. Simple methods to do this can be thresholding
at the mean or mean-plus/minus-std. Of course, you may also
try the MI computation for continuous variables directly, as
I showed in the my paper (below). However, typically the results
are not as good as discrete variables.
Note that this version is old and uses double-precision
in mutual information computation, thus the feature selection
results may be slightly different if you also compare the
against those produced by the newer C versions downloadable
from our website. The C versions uses single precision for
floating numbers to save some memories.
The codes cannot be re-distributed without permission from
the author, Hanchuan Peng.
We hope you cite our work as follows, which you can download
the paper at Hanchuan Peng's web site http://research.janelia.org/peng
(you may google and find out the latest website).
Hanchuan Peng, Fuhui Long, and Chris Ding, "Feature selection
based on mutual information: criteria of max-dependency,
max-relevance, and min-redundancy," IEEE Transactions on
Pattern Analysis and Machine Intelligence, Vol. 27, No. 8,
pp.1226-1238, 2005.
Should you have any question, please send email to
hanchuan.peng@gmail.com or pengh@janelia.hhmi.org .
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