Rank: 1886 based on 30 downloads (last 30 days) and 3 files submitted
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Siqing Wu

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Speech processing, pattern classification, affective computing

 

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31 Jul 2008 Principal components analysis (PCA) Principal components analysis (PCA) Author: Siqing Wu pca, principal components ..., probability, statistics, eigenvalue 14 3
  • 1.0
1.0 | 1 rating
31 Jul 2008 Screenshot Legendre polynomial fitting Fitting data using a linear combination of Legendre polynomials Author: Siqing Wu legendre polynomials, linear regression, curve fitting, mathematics 13 2
30 Jul 2008 Vandermonde matrix Creating matrix with terms of geometric progression in each row Author: Siqing Wu geometric progression, linear algebra, vandermonde matrix, mathematics 3 1
  • 4.0
4.0 | 1 rating
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10 Dec 2008 Vandermonde matrix Creating matrix with terms of geometric progression in each row Author: Siqing Wu Davis, Tim

You should clarify in your help information that the matrix produced has its columns listed in the opposite order of the vander.m function in MATLAB.

Also, providing the determinant is nice, but it would be better to return the log or log10 of the determinant. That way, you can deal with determinants that are very large and very small (it's quite easy for det(A) to underflow or overflow).

Otherwise, nice work, and nicely commented. (You should have a "see also vander" line, though).

Nice use of cumprod. I'm surpised that this function is slower than vander, though.
01 Aug 2008 Legendre polynomial fitting Fitting data using a linear combination of Legendre polynomials Author: Siqing Wu Wu, Siqing

Hi, Dimitri Shvorob

I am a little too lazy to compare my file to every existing file before :-) (which, however, doesn't mean I haven't tried to do so). I just assume that most people who spend one minute looking at the description or help of the file would quickly find the answer to the question that if this is the code they want. I left the decision to them (which, some people may prefer while others don't). Anyway, thank you for you advice, and I also appreciate that this time you didn't give me one star :P

Regards,
01 Aug 2008 Legendre polynomial fitting Fitting data using a linear combination of Legendre polynomials Author: Siqing Wu Shvorob, Dimitri

Again, if you are submitting something that apparently has been available on FEX before you -
http://www.mathworks.co.uk/matlabcentral/fileexchange/loadFile.do?objectId=4710&objectType=file
is one of a dozen submissions I found - it's a good idea to (a) link to earlier submissions, and (b) explain how yours is different/better. Is it?
30 Jul 2008 Principal components analysis (PCA) Principal components analysis (PCA) Author: Siqing Wu Wu, Siqing

BTW, I found I forgot to correct one testing line (though it won't do anything wrong)... Since it takes one or two days to make the updated file posted, You may want to change "D = [D./max(D); zeros(N-nc,1)];" to "D = D/max(D);" Sorry about this.
30 Jul 2008 Principal components analysis (PCA) Principal components analysis (PCA) Author: Siqing Wu Wu, Siqing

Hi, Dimitri Shvorob

Basically I think the purposes of my code and that toolbox are a little different, e.g. if you have a bunch of features and wanna reduce the dimensionality of the dataset, maybe this single code is a little more straightforward and easy to use.

Regards,
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mathematics, curve fitting, eigenvalue, geometric progression, legendre polynomials
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Updated   File Tags Downloads
(last 30 days) 		Comments Rating
31 Jul 2008 Principal components analysis (PCA) Principal components analysis (PCA) Author: Siqing Wu pca, principal components ..., probability, statistics, eigenvalue 14 3
  • 1.0
1.0 | 1 rating
31 Jul 2008 Screenshot Legendre polynomial fitting Fitting data using a linear combination of Legendre polynomials Author: Siqing Wu legendre polynomials, linear regression, curve fitting, mathematics 13 2
30 Jul 2008 Vandermonde matrix Creating matrix with terms of geometric progression in each row Author: Siqing Wu geometric progression, linear algebra, vandermonde matrix, mathematics 3 1
  • 4.0
4.0 | 1 rating

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