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A Generalized Maximum Entropy Approach To Bregman Co Clustering
- 1. Author : Arindam Banerjee, Inderjit Dhillon, Joydeep Ghosh, Srujana Merugu, and Dharmendra S. Modha Source : KDD ’04, August 22-25, 2004, ACM, pp. 509- pp.514 Presenter : Allen Wu 03/12/10
- 5. 0.18 0.18 0.14 0.14 0.18 0.18 0.15 0.15 0.15 0.15 0.2 0.2 0.5 0.5 0.3 0.3 0.4 03/12/10
- 6. 03/12/10 D(p||q) 0.041909 0.041909 0.05696 0.05696 0.0376 0.049641 D(p||q) 0.05696 0.05696 0.04191 0.04191 0.049641 0.0376
- 7. 03/12/10 D(p||q) 0.02118 0.02118 0.02243 0.040765 0.04893 0.04893 D(p||q) 0.048138 0.048138 0.041942 0.02295 0.02052 0.02052
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Editor's Notes
- Row-cluster prototyppe q t (Y|^x) is closest to P(Y|x) in Kullback-Leibler divergence.
- The algorithm keeps iterating Step2 through 5 until some desired convergence condition is met.
- Distortion 失真