Using GPUs for Machine Learning Algorithms
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
A performance study of general-purpose applications on graphics processors using CUDA
Journal of Parallel and Distributed Computing
Exploring the multiple-GPU design space
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Facial Expression Recognition Based on NMF and SVM
IFITA '09 Proceedings of the 2009 International Forum on Information Technology and Applications - Volume 03
Using underapproximations for sparse nonnegative matrix factorization
Pattern Recognition
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
A hybrid face recognition approach using GPUMLib
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
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Non-Negative Matrix Factorization (NMF) algorithms decompose a matrix, containing only non-negative coefficients, into the product of two matrices, usually with reduced ranks. The resulting matrices are constrained to have only non-negative coefficients. NMF can be used to reduce the number of characteristics in a dataset, while preserving the relevant information that allows for the reconstruction of the original data. Since negative coefficients are not allowed, the original data is reconstructed through additive combinations of the parts-based factorized matrix representation. A Graphics Processing Unit (GPU) implementation of the NMF algorithms, using both the multiplicative and the additive (gradient descent) update rules is presented for the Euclidean distance as well as for the divergence cost function. The performance results on an image database demonstrate extremely high speedups, making the GPU implementations excel by far the CPU implementations.