User contributions for Myakhave
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14 August 2009
- 18:4918:49, 14 August 2009 diff hist +20 convex and Semi Nonnegative Matrix Factorization →SVD, Convex-NMF and Semi-NMF Comparison
- 18:4218:42, 14 August 2009 diff hist +35 convex and Semi Nonnegative Matrix Factorization →SVD, Convex-NMF and Semi-NMF Comparison
- 18:4018:40, 14 August 2009 diff hist −2 convex and Semi Nonnegative Matrix Factorization →SVD, Convex-NMF and Semi-NMF Comparison
- 18:4018:40, 14 August 2009 diff hist +144 convex and Semi Nonnegative Matrix Factorization →SVD, Convex-NMF and Semi-NMF comparison
- 18:2718:27, 14 August 2009 diff hist +541 convex and Semi Nonnegative Matrix Factorization →Convex NMF
13 August 2009
- 21:0921:09, 13 August 2009 diff hist +92 maximum-Margin Matrix Factorization →A short discussion on Loss Function for classification
- 20:3720:37, 13 August 2009 diff hist +3 maximum-Margin Matrix Factorization →Hinge Loss Function
- 20:3220:32, 13 August 2009 diff hist +12 maximum-Margin Matrix Factorization →Limitation
- 19:2319:23, 13 August 2009 diff hist +302 maximum-Margin Matrix Factorization →Experiments
28 July 2009
- 21:1621:16, 28 July 2009 diff hist −154 relevant Component Analysis →First paper: Shental et al., 2002 N. Shental, T. Hertz, D. Weinshall, and M. Pavel, "Adjustment Learning and Relevant Component Analysis," Proc. European Conference on Computer Vision (ECCV), 2002, pp. 776-790.
- 21:1021:10, 28 July 2009 diff hist +410 relevant Component Analysis →First paper: Shental et al., 2002 N. Shental, T. Hertz, D. Weinshall, and M. Pavel, "Adjustment Learning and Relevant Component Analysis," Proc. European Conference on Computer Vision (ECCV), 2002, pp. 776-790.
- 20:5220:52, 28 July 2009 diff hist −16 relevant Component Analysis →First paper: Shental et al., 2002 N. Shental, T. Hertz, D. Weinshall, and M. Pavel, "Adjustment Learning and Relevant Component Analysis," Proc. European Conference on Computer Vision (ECCV), 2002, pp. 776-790.
- 20:4920:49, 28 July 2009 diff hist −381 relevant Component Analysis →First paper: Shental et al., 2002 N. Shental, T. Hertz, D. Weinshall, and M. Pavel, "Adjustment Learning and Relevant Component Analysis," Proc. European Conference on Computer Vision (ECCV), 2002, pp. 776-790.
- 20:4320:43, 28 July 2009 diff hist +11 relevant Component Analysis →First paper: Shental et al., 2002 N. Shental, T. Hertz, D. Weinshall, and M. Pavel, "Adjustment Learning and Relevant Component Analysis," Proc. European Conference on Computer Vision (ECCV), 2002, pp. 776-790.
- 20:4320:43, 28 July 2009 diff hist −9 relevant Component Analysis →First paper: Shental et al., 2002 N. Shental, T. Hertz, D. Weinshall, and M. Pavel, "Adjustment Learning and Relevant Component Analysis," Proc. European Conference on Computer Vision (ECCV), 2002, pp. 776-790.
- 20:4220:42, 28 July 2009 diff hist −4 relevant Component Analysis →First paper: Shental et al., 2002 N. Shental, T. Hertz, D. Weinshall, and M. Pavel, "Adjustment Learning and Relevant Component Analysis," Proc. European Conference on Computer Vision (ECCV), 2002, pp. 776-790.
- 20:4120:41, 28 July 2009 diff hist −2 relevant Component Analysis →First paper: Shental et al., 2002 N. Shental, T. Hertz, D. Weinshall, and M. Pavel, "Adjustment Learning and Relevant Component Analysis," Proc. European Conference on Computer Vision (ECCV), 2002, pp. 776-790.
- 20:4020:40, 28 July 2009 diff hist +577 relevant Component Analysis →First paper: Shental et al., 2002 N. Shental, T. Hertz, D. Weinshall, and M. Pavel, "Adjustment Learning and Relevant Component Analysis," Proc. European Conference on Computer Vision (ECCV), 2002, pp. 776-790.
- 17:5717:57, 28 July 2009 diff hist +211 visualizing Data using t-SNE →Compensation for Mismatched Dimensionality by Mismatched Tails
- 17:4017:40, 28 July 2009 diff hist +134 visualizing Data using t-SNE →The Crowding Problem
- 17:3417:34, 28 July 2009 diff hist +2 visualizing Data using t-SNE →The Crowding Problem
- 17:3117:31, 28 July 2009 diff hist +2 visualizing Data using t-SNE →The Crowding Problem
- 17:3117:31, 28 July 2009 diff hist +2 visualizing Data using t-SNE →The Crowding Problem
- 17:3017:30, 28 July 2009 diff hist +250 visualizing Data using t-SNE →The Crowding Problem
- 17:1717:17, 28 July 2009 diff hist +135 visualizing Data using t-SNE →The Crowding Problem
10 July 2009
- 20:2620:26, 10 July 2009 diff hist +12 visualizing Similarity Data with a Mixture of Maps →Modeling Human Word Association Data
- 20:2120:21, 10 July 2009 diff hist +179 visualizing Similarity Data with a Mixture of Maps →Modeling Human Word Association Data
- 20:1520:15, 10 July 2009 diff hist +386 visualizing Similarity Data with a Mixture of Maps →Modeling Human Word Association Data
- 19:4819:48, 10 July 2009 diff hist 0 independent Component Analysis: algorithms and applications →Finding hidden factors in financial data
- 19:4719:47, 10 July 2009 diff hist +231 independent Component Analysis: algorithms and applications →Finding hidden factors in financial data
- 19:4419:44, 10 July 2009 diff hist +679 independent Component Analysis: algorithms and applications →Applications
- 19:2719:27, 10 July 2009 diff hist +1 independent Component Analysis: algorithms and applications →Why Gaussian variables are forbidden
- 19:2119:21, 10 July 2009 diff hist −169 independent Component Analysis: algorithms and applications →Independence versus uncorrelatedness
- 19:2019:20, 10 July 2009 diff hist +313 independent Component Analysis: algorithms and applications →Independence versus uncorrelatedness
- 19:1619:16, 10 July 2009 diff hist +398 independent Component Analysis: algorithms and applications →Independence versus uncorrelatedness
- 19:0619:06, 10 July 2009 diff hist +246 independent Component Analysis: algorithms and applications →Why Gaussian variables are forbidden
- 11:4411:44, 10 July 2009 diff hist +4 visualizing Similarity Data with a Mixture of Maps →Stochastic Neighbour Embedding
- 11:3911:39, 10 July 2009 diff hist +20 visualizing Similarity Data with a Mixture of Maps →Stochastic Neighbour Embedding
- 11:3811:38, 10 July 2009 diff hist +592 visualizing Similarity Data with a Mixture of Maps →Stochastic Neighbour Embedding
- 11:1311:13, 10 July 2009 diff hist +1 visualizing Similarity Data with a Mixture of Maps →Introduction
- 10:5910:59, 10 July 2009 diff hist +147 nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization →Experimental Results
- 10:5610:56, 10 July 2009 diff hist +74 graph Laplacian Regularization for Larg-Scale Semidefinite Programming →Results
- 10:5510:55, 10 July 2009 diff hist 0 graph Laplacian Regularization for Larg-Scale Semidefinite Programming →Results
- 10:5310:53, 10 July 2009 diff hist +37 graph Laplacian Regularization for Larg-Scale Semidefinite Programming →Results
8 July 2009
- 23:2523:25, 8 July 2009 diff hist +165 graph Laplacian Regularization for Larg-Scale Semidefinite Programming →Introduction
- 23:2223:22, 8 July 2009 diff hist +14 nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization →References
- 23:2123:21, 8 July 2009 diff hist 0 nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization →Introduction
- 23:2023:20, 8 July 2009 diff hist +16 nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization →Experimental Results
- 23:2023:20, 8 July 2009 diff hist +260 nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization →Introduction
- 23:1923:19, 8 July 2009 diff hist +12 graph Laplacian Regularization for Larg-Scale Semidefinite Programming →Introduction