Grassmann Clustering
Grassmann Clustering
Grassmann Clustering
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n(t)<br />
s(t) �<br />
A<br />
F. Theis<br />
x(t)<br />
III. Projective <strong>Clustering</strong><br />
Applications & Extensions<br />
• robustness of ICA<br />
• similar to [Meinecke et al, 2003]<br />
• determine „best“ directions in data set<br />
• hyperplane clustering<br />
• instead of line vectors cluster<br />
normal vectors<br />
• projective k-median clustering<br />
• median is statistically more robust!<br />
data set<br />
bootstrap<br />
matrix A matrix A<br />
best mixing<br />
directions<br />
22<br />
different sample subsets<br />
projective clustering<br />
Apr 6, 2006 :: Tübingen
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