MapReduce implementation of Variational Bayesian Probabilistic Matrix Factorization algorithm

Tewari, Naveen; Koduvely, Hari M.; Guha, Sarbendu; Yadav, Arun Kumar; David, Gladbin

doi:10.1109/bigdata.2013.6691747

Cited by 5 publications

(2 citation statements)

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“…The factor U (s)T U (s) in (U (n)T U (n) ) ⊛ −n is expressed by the summation over all {U (s)T m U (s) m }. From (17), (18), and (19), we can derive the rule for computing the term Y (n) U ⊙ −n for any 1 ≤ s ≤ N. Hence,…”

Section: Distributed Ntfmentioning

confidence: 99%

“…Implementations of the TSQR are available in several environments, including MapReduce, distributed memory MPI, and GPUs. Previous studies have also used the MapReduce paradigm for scaling up various matrix factorization methods, eg, based on the alternating least squares (ALS) algorithm with broadcast‐joints, stochastic gradient descent updates, variational Bayesian algorithms, or in the R and SPSS statistical tools . Large‐scale matrix factorizations with distributed ALS algorithms have also been implemented on multiple GPUs…”

Section: Introductionmentioning

confidence: 99%

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Distributed geometric nonnegative matrix factorization and hierarchical alternating least squares–based nonnegative tensor factorization with the MapReduce paradigm

Zdunek

Fonał

2018

Concurrency and Computation

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Summary Nonnegative matrix factorization and its multilinear extension known as nonnegative tensor factorization are commonly used methods in machine learning and data analysis for feature extraction and dimensionality reduction for nonnegative high‐dimensional data. Dimensionality reduction for massive amounts of data usually involves distributed computation across multi‐node computer architectures. In this study, we propose various computational strategies for parallel and distributed computation of the latent factors in both factorization models, all of which are based on partitioning the computational tasks according to the MapReduce paradigm. We extend the previously reported distributed hierarchical alternating least squares algorithm to the multi‐way array factorization model, where we assume that the observed multi‐way data can be partitioned into chunks along one mode. Moreover, we propose a new geometry‐based distributed computational strategy for solving nonnegative matrix factorization problems. Numerical experiments performed using various large‐scale data sets demonstrated that these algorithms are efficient and robust to noisy data.

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Section: Distributed Ntfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%