PPT-Distributed Nonnegative Matrix Factorization for Web-Scale

Author : liane-varnes | Published Date : 2016-03-30

Data Analysis on MapReduce Chao Liu Hung chih Yang Jinliang Fan LiWei He YiMin Wang Internet Services Research Center ISRC Microsoft Research Redmond Internet Services

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Distributed Nonnegative Matrix Factorization for Web-Scale: Transcript

Data Analysis on MapReduce Chao Liu Hung chih Yang Jinliang Fan LiWei He YiMin Wang Internet Services Research Center ISRC Microsoft Research Redmond Internet Services Research Center ISRC. Lee Bell Laboratories Lucent Technologies Murray Hill NJ 07974 H Sebastian Seung y Dept of Brain and Cog Sci Massachusetts Institute of Technology Cambridge MA 02138 Abstract Nonnegative matrix factorization NMF has previously been shown to be a Hoyer PATRIK HOYER HELSINKI FI HIIT Basic Research Unit Department of Computer Science PO Box 68 FIN00014 University of Helsinki Finland Editor Peter Dayan Abstract Nonnegative matrix factorization NMF is a recently deve loped technique for 64257ndi Unsupervised Learning. Sanjeev . Arora. Princeton University. Computer Science + Center for Computational Intractability. Maryland Theory Day 2014. (Funding: NSF and Simons Foundation). Supervised . vs. Recovering latent factors in a matrix. m. movies. v11. …. …. …. vij. …. vnm. V[. i,j. ] = user i’s rating of movie j. n . users. Recovering latent factors in a matrix. m. movies. n . users. T(A) . 1. 2. 3. 4. 6. 7. 8. 9. 5. 5. 9. 6. 7. 8. 1. 2. 3. 4. 1. 5. 2. 3. 4. 9. 6. 7. 8. A . 9. 1. 2. 3. 4. 6. 7. 8. 5. G(A) . Symmetric-pattern multifrontal factorization. T(A) . 1. 2. 3. 4. 6. 7. 8. under Additional Constraints. Kaushik . Mitra. . University . of Maryland, College Park, MD . 20742. Sameer . Sheorey. y. Toyota Technological Institute, . Chicago. Rama . Chellappa. University of Maryland, College Park, MD 20742. Zhenhong. Chen, . Yanyan. . Lan. , . Jiafeng. . Guo. , Jun . Xu. , and . Xueqi. Cheng . CAS Key Laboratory of Network Data Science and Technology,. Institute . of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. and. Collaborative Filtering. 1. Matt Gormley. Lecture . 26. November 30, 2016. School of Computer Science. Readings:. Koren. et al. (2009). Gemulla. et al. (2011). 10-601B Introduction to Machine Learning. Grayson Ishihara. Math 480. April 15, 2013. Topics at Hand. What is Partial Pivoting?. What is the PA=LU Factorization?. What kinds of things can we use these tools for?. Partial Pivoting. Used to solve matrix equations. m. movies. v11. …. …. …. vij. …. vnm. V[. i,j. ] = user i’s rating of movie j. n . users. Recovering latent factors in a matrix. m. movies. n . users. m. movies. x1. y1. x2. y2. ... ... …. Sebastian . Schelter. , . Venu. . Satuluri. , Reza . Zadeh. Distributed Machine Learning and Matrix Computations workshop in conjunction with NIPS 2014. Latent Factor Models. Given . M. sparse. n . x . Dileep Mardham. Introduction. Sparse Direct Solvers is a fundamental tool in scientific computing. Sparse factorization can be a challenge to accelerate using GPUs. GPUs(Graphics Processing Units) can be quite good for accelerating sparse direct solvers. Gemar. 11-10-12. Advisor: Dr. . Rebaza. Overview. Definitions. Theorems. Proofs. Examples. Physical Applications. Definition 1. We say that a subspace S or . R. n. is invariant under . A. nxn. , or A-invariant if:. Sebastian . Schelter. , . Venu. . Satuluri. , Reza . Zadeh. Distributed Machine Learning and Matrix Computations workshop in conjunction with NIPS 2014. Latent Factor Models. Given . M. sparse. n . x .

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