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 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. by Carol Edelstein. Definition. Product. – An answer to a multiplication problem.. . 7 x 8 = 56. Product. Definition. Factor. – a number that is multiplied by another to give a product.. . 7 x 8 = 56. Author: Maximilian Nickel. Speaker: . Xinge. Wen. INTRODUCTION . –. Multi relational Data. Relational data is everywhere in our life:. WEB. Social networks. Bioinformatics. INTRODUCTION . –. Why Tensor . 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. 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 . m. columns. v11. …. …. …. vij. …. vnm. n . rows. 2. Recovering latent factors in a matrix. K * m. n * K. x1. y1. x2. y2. ... ... …. …. xn. yn. a1. a2. ... …. am. b1. b2. …. …. bm. v11. Inference. Dave Moore, UC Berkeley. Advances in Approximate Bayesian Inference, NIPS 2016. Parameter Symmetries. . Model. Symmetry. Matrix factorization. Orthogonal. transforms. Variational. . a. 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. Georgina . Hall. Princeton, . ORFE. Joint work with . Amir Ali Ahmadi. Princeton, ORFE. 1. 5/4/2016. IBM May 2016. Nonnegative and convex polynomials. A polynomial . is nonnegative if . How does . nonnegativity. ORTHOGONALIZATION AND. LEAST SQUARES. -Mohammed. BEST GROUP. CONTENTS. Householder and Givens Transformations. The QR Factorization. The Full-Rank Least Squares Problem. Other Orthogonal Factorizations. . SYFTET. Göteborgs universitet ska skapa en modern, lättanvänd och . effektiv webbmiljö med fokus på användarnas förväntningar.. 1. ETT UNIVERSITET – EN GEMENSAM WEBB. Innehåll som är intressant för de prioriterade målgrupperna samlas på ett ställe till exempel:.

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