Presentation - MIV
Presentation - MIV
Presentation - MIV
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collet@lsiit.u-strasbg.fr<br />
iAstro Workshop - Nice Observatory<br />
16/17 October 2003<br />
Reduction using local projections<br />
(1 st technique)<br />
Local projections<br />
On each cluster established by the grouping step, we perform one<br />
of the two projections:<br />
PCA:<br />
Seeks data variance maximisation. Projection matrix given by the<br />
eigen vectors of the covariance matrix of data.<br />
PCA /ICA<br />
ICA:<br />
We use the fastICA algorithm with deflationary orthogonalization<br />
which seeks maximisation of the nongaussianity<br />
Finally, one keeps only the first image corresponding to the higher<br />
eigenvalue (PCA) or to the higher nongaussianity criterion (ICA).
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