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21I will discuss recent work on randomized algorithms for low-rank approximation and principal component analysis (PCA). The talk will focus on efforts that move beyond the extremely fast, but relatively crude approximatio

I will discuss recent work on randomized algorithms for low-rank approximation and principal component analysis (PCA). The talk will focus on efforts that move beyond the extremely fast, but relatively crude approximatio

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Source URL: mmds-data.org

Language: English - Date: 2016-06-23 15:50:48
22Computing the Complete CS Decomposition Brian D. Sutton∗ May 20, 2008 Abstract An algorithm for computing the complete CS decomposition of a partitioned unitary matrix is developed. Although the existence of the CS dec

Computing the Complete CS Decomposition Brian D. Sutton∗ May 20, 2008 Abstract An algorithm for computing the complete CS decomposition of a partitioned unitary matrix is developed. Although the existence of the CS dec

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Source URL: faculty.rmc.edu

Language: English - Date: 2008-05-20 14:06:05
23Distributed Community Detection on Edge-labeled Graphs using Spark San-Chuan Hung Miguel Araujo

Distributed Community Detection on Edge-labeled Graphs using Spark San-Chuan Hung Miguel Araujo

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Source URL: www.mlgworkshop.org

Language: English - Date: 2016-08-14 01:22:56
24Computer Vision  Exercise Session 2 Institute of Visual Computing

Computer Vision Exercise Session 2 Institute of Visual Computing

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Source URL: www.cvg.ethz.ch

Language: English - Date: 2015-10-06 15:10:50
25CS168: The Modern Algorithmic Toolbox Lecture #8: How PCA Works Tim Roughgarden & Gregory Valiant∗ April 20,

CS168: The Modern Algorithmic Toolbox Lecture #8: How PCA Works Tim Roughgarden & Gregory Valiant∗ April 20,

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Source URL: theory.stanford.edu

Language: English - Date: 2016-06-04 09:49:44
26A network analysis of committees in the U.S. House of Representatives Mason A. Porter*†‡, Peter J. Mucha*, M. E. J. Newman§, and Casey M. Warmbrand¶ *School of Mathematics, Georgia Institute of Technology, Atlanta,

A network analysis of committees in the U.S. House of Representatives Mason A. Porter*†‡, Peter J. Mucha*, M. E. J. Newman§, and Casey M. Warmbrand¶ *School of Mathematics, Georgia Institute of Technology, Atlanta,

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Source URL: www-personal.umich.edu

Language: English - Date: 2005-05-16 22:04:47
27Appendix A: Face subclasses In Fig. 3, we showed an example of features versus context. In that example, we illustrated the idea with the class “eyes.” In Fig. S1 we show the subclasses for the class “face” and t

Appendix A: Face subclasses In Fig. 3, we showed an example of features versus context. In that example, we illustrated the idea with the class “eyes.” In Fig. S1 we show the subclasses for the class “face” and t

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Source URL: www2.ece.ohio-state.edu

Language: English - Date: 2010-02-11 05:58:43
28GEOMETRY OF FEASIBLE SPACES OF TENSORS  A Dissertation by YANG QI

GEOMETRY OF FEASIBLE SPACES OF TENSORS A Dissertation by YANG QI

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Source URL: www.math.tamu.edu

Language: English - Date: 2013-07-24 16:18:51
29Lighting Sensitive Display SHREE K. NAYAR Columbia University, New York PETER N. BELHUMEUR Columbia University, New York and

Lighting Sensitive Display SHREE K. NAYAR Columbia University, New York PETER N. BELHUMEUR Columbia University, New York and

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Source URL: www.vast.uccs.edu

Language: English - Date: 2004-06-08 11:54:04
30BIT 39(1), pp. 143–151, 1999 ILL-CONDITIONEDNESS NEEDS NOT BE COMPONENTWISE NEAR TO ILL-POSEDNESS FOR LEAST SQUARES PROBLEMS SIEGFRIED M. RUMP∗ Abstract. The condition number of a problem measures the sensitivity of

BIT 39(1), pp. 143–151, 1999 ILL-CONDITIONEDNESS NEEDS NOT BE COMPONENTWISE NEAR TO ILL-POSEDNESS FOR LEAST SQUARES PROBLEMS SIEGFRIED M. RUMP∗ Abstract. The condition number of a problem measures the sensitivity of

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Source URL: www.ti3.tu-harburg.de

Language: English - Date: 2005-11-23 05:51:43