Grassmann Clustering

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n(t) s(t) � A F. Theis x(t) has many applications - for example for feature extraction of immunological data sets I. Introduction <strong>Clustering</strong> [ Theis, Hartl, Krauss-Etschmann, Lang. Neural networksignal analysis in immunology. Proc. ISSPA2003. ] 0.426 0.248 0.07 49 35.9 d 22.8 22.1 10.9 d 0.718 53.8 d 4 27.9 9.29 CB(3) 4.44 d 0.0623 CB(3) ILD(1) CB(1) ILD(2) ILD(2) CB(1) CB(1) ILD(2) ILD(3) ILD(1) CB(1) ILD(2) CB(2) CB(1) CB(1) ILD(1) CB(2) ILD(1) ILD(1) ILD(2) ILD(1) ILD(1) CB(1) 1.89 1 d 0.166 3.3 1.81 d 0.395 32.1 16.3 d 1.39 5.22 3.28 d 1.35 nO(2) O(1) nO(3) x(1) O(1) x(2) x(2) O(1) O(1) x(2) x(3) O(2) nO(1) O(1) x(1) O(1) x(2) x(1) x(1) x(1) x(1) O(2) x(1) x(2) O(1) 13.6 8.23 d 2.8 4.5 1.8 d 0.104 82.6 57.2 d 30.4 699000 446000 d 196000 6 48.1 30.3 d 15.5 37.8 19.1 d 3.2 25.5 16 d 6.84 1.71 1.33 d 1 K−means−Clusters Apr 6, 2006 :: Tübingen
n(t) s(t) � A F. Theis x(t) I. Introduction Example: read digits! task: differentiate between and use unsupervised learning 7 Apr 6, 2006 :: Tübingen
Page 1 and 2: n(t) s(t) � A x(t) Grassmann Clus
Page 3 and 4: n(t) s(t) � A F. Theis x(t) I. In
Page 5: n(t) s(t) � A F. Theis x(t) I. In
Page 9 and 10: n(t) s(t) � A F. Theis x(t) Examp
Page 11 and 12: n(t) s(t) � A F. Theis x(t) dimen
Page 13 and 14: n(t) s(t) � A F. Theis x(t) • s
Page 21 and 22: n(t) s(t) � A F. Theis x(t) „di
Page 29 and 30: n(t) s(t) � A F. Theis 3 x(t) hie
Page 32 and 33: n(t) s(t) � A F. Theis x(t) II. P
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Page 39: n(t) s(t) � A F. Theis x(t) III.
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Page 51 and 52: n(t) s(t) � A F. Theis p=3 x(t) P
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n(t) s(t) � A F. Theis x(t) IV. G
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n(t) s(t) � A F. Theis x(t) V. Su
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n(t) s(t) � A 5 4.5 4 3.5 3 2.5 2
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n(t) s(t) � A F. Theis x(t) Concl
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n(t) s(t) � A F. Theis x(t) Concl
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Grassmann Clustering

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