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Hanchuan Peng

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23 Aug 2007 Mutual Information computation A self-contained package for computing mutual information, joint/conditional probability, entropy Author: Hanchuan Peng mutual information, joint probability, mutual infromation co..., conditional probabili..., probability, a nice piece of work 376 52
  • 4.68966
4.7 | 30 ratings
09 May 2007 minimum-redundancy maximum-relevance feature selection The source codes of minimum redundancy feature selection Author: Hanchuan Peng feature selection, mutual information, minimumredundancy, probability, statistics 110 6
  • 4.6
4.6 | 5 ratings
19 Apr 2007 mRMR Feature Selection (using mutual information computation) This is a cross-platform version of mimimum-redundancy maximum-relevancy feature selection Author: Hanchuan Peng feature selection, minimum redundancy, biotech, pharmaceutical, maximum relevance 172 21
  • 4.69231
4.7 | 14 ratings
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30 Apr 2013 minimum-redundancy maximum-relevance feature selection The source codes of minimum redundancy feature selection Author: Hanchuan Peng Malviya, Tripti

Please tell me how to use it.
I have the term-document matrix(i used tmg tool box to generate it) of type double and i m unable to pass it as the first parameter to the function in mrmr_mid_d.m .
I have to apply this on a text data set.
Please help!!
05 Mar 2013 Mutual Information computation A self-contained package for computing mutual information, joint/conditional probability, entropy Author: Hanchuan Peng Davis

Maybe I'm missing something, but I think this code produces weird behavior with vectors of low values, e.g. returning entropy of 0 for [1:10] * .0001.

Run this code and see for yourself:
a = [];
for i = 1:20e4
a(i) = entropy([1:10] .* i/10e4);
end
plot(a)
set(gca,'xticklabel',[0:.2:2])
ylabel('Est. Entropy')
xlabel('Scaling factor')
06 Feb 2013 mRMR Feature Selection (using mutual information computation) This is a cross-platform version of mimimum-redundancy maximum-relevancy feature selection Author: Hanchuan Peng Athavale, Yashodhan

I'm using this algorithm for the first time, and unable to relate the articles published and the code written. As indicated in the function [fea] = mrmr_miq_d(d, f, K):

I did the following:
d= 180 x 48 dataset - indicating 180 samples each having 48 features
f = 180 x 1 - class/category of the n samples
K = 24 - number of features to be selected

I ran the code and got the [fea] as output vector of 1 x 24 dimensions. But what does [fea] mean ? What do the numbers in the fea array signify ?
08 Jan 2013 mRMR Feature Selection (using mutual information computation) This is a cross-platform version of mimimum-redundancy maximum-relevancy feature selection Author: Hanchuan Peng Wang, Jing

http://t.cn/zjupBbJ All files and possible problems and answers could be seen in this webpage. Good luck!
08 Jan 2013 mRMR Feature Selection (using mutual information computation) This is a cross-platform version of mimimum-redundancy maximum-relevancy feature selection Author: Hanchuan Peng Wang, Jing

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feature selection, mutual information, probability, statistics, biotech
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Updated   File Tags Downloads
(last 30 days) 		Comments Rating
23 Aug 2007 Mutual Information computation A self-contained package for computing mutual information, joint/conditional probability, entropy Author: Hanchuan Peng mutual information, joint probability, mutual infromation co..., conditional probabili..., probability, a nice piece of work 376 52
  • 4.68966
4.7 | 30 ratings
09 May 2007 minimum-redundancy maximum-relevance feature selection The source codes of minimum redundancy feature selection Author: Hanchuan Peng feature selection, mutual information, minimumredundancy, probability, statistics 110 6
  • 4.6
4.6 | 5 ratings
19 Apr 2007 mRMR Feature Selection (using mutual information computation) This is a cross-platform version of mimimum-redundancy maximum-relevancy feature selection Author: Hanchuan Peng feature selection, minimum redundancy, biotech, pharmaceutical, maximum relevance 172 21
  • 4.69231
4.7 | 14 ratings

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