Presentation - MIV
Presentation - MIV
Presentation - MIV
You also want an ePaper? Increase the reach of your titles
YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.
collet@lsiit.u-strasbg.fr<br />
iAstro Workshop - Nice Observatory<br />
16/17 October 2003<br />
Dimensionality reduction<br />
ICA principles<br />
* Model of source mixture (« cocktail party problem »)<br />
* linear transform making the data components independent<br />
* Mutual information measured by Kullback-Leibler distance<br />
* Weak mutual information between sources : Neguentropy<br />
(non gaussianity criterion)<br />
* pre-processing : centered data, spherical noise<br />
* loss of source order<br />
*lossof source power<br />
ICA’s methods<br />
* Cumulant-based approach (Comon)<br />
* Jade (4th order cumulant + joint diagonalization), (Carodoso, Souloumiac)<br />
* Infomax : Neural Network (Bell, Sejnowski) ;<br />
* FastICA (Oja & Hyvärinen),<br />
* SOBI : cross-correlation + joint diagonalization (Belouchrani)…
Change language