nmfgpu4R: GPU-Accelerated Computation of the Non-Negative Matrix Factorization (NMF) Using CUDA Capable Hardware

In this work, a novel package called nmfgpu4R is presented, which offers the computation of Non-negative Matrix Factorization (NMF) on Compute Unified Device Architecture (CUDA) platforms within the R environment. Benchmarks show a remarkable speed-up in terms of time per iteration by utilizing the parallelization capabilities of modern graphics cards. Therefore the application of NMF gets more attractive for real-world sized problems because the time to compute a factorization is reduced by an order of magnitude.

[1]  Renaud Gaujoux,et al.  A flexible R package for nonnegative matrix factorization , 2010, BMC Bioinformatics.

[2]  Michael W. Berry,et al.  Algorithms and applications for approximate nonnegative matrix factorization , 2007, Comput. Stat. Data Anal..

[3]  P. Paatero,et al.  Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values† , 1994 .

[4]  Christos Boutsidis,et al.  SVD based initialization: A head start for nonnegative matrix factorization , 2008, Pattern Recognit..

[5]  Noel Lopes,et al.  A fast optimized semi-supervised non-negative Matrix Factorization algorithm , 2011, The 2011 International Joint Conference on Neural Networks.

[6]  Kristin J. Dana,et al.  Compact representation of bidirectional texture functions , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[7]  Vipin Kumar UNDERSTANDING COMPLEX DATASETS: DATA MINING WITH MATRIX DECOMPOSITIONS , 2006 .

[8]  Michael W. Berry,et al.  Document clustering using nonnegative matrix factorization , 2006, Inf. Process. Manag..

[9]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[10]  R. Fisher THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[11]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[12]  Amy Nicole Langville,et al.  Algorithms, Initializations, and Convergence for the Nonnegative Matrix Factorization , 2014, ArXiv.

[13]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  E. M. Wright,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

[15]  Noel Lopes,et al.  Non-negative Matrix Factorization Implementation Using Graphic Processing Units , 2010, IDEAL.

[16]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[17]  V. P. Pauca,et al.  Nonnegative matrix factorization for spectral data analysis , 2006 .

[18]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[19]  Asoke K. Nandi,et al.  An enhanced initialization method for non-negative matrix factorization , 2013, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

[20]  Dietrich Lehmann,et al.  Nonsmooth nonnegative matrix factorization (nsNMF) , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Yin Zhang,et al.  Accelerating the Lee-Seung Algorithm for Nonnegative Matrix Factorization , 2005 .

[22]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.