A GPU-accelerated non-negative sparse latent semantic analysis algorithm for social tagging data

Nowadays large-scale social tagging data have become very valuable in organizing and indexing multimedia resources. In this paper, we apply Non-negative Sparse Latent Semantic Analysis (NN-Sparse LSA) to discover the latent semantic space behind associations between multimedia resources and tagging data. Based on the traditional coordinate-descent algorithm, column-orthogonality and non-negative constraints, we derive a much faster optimization algorithm in theory for solving the NN-Sparse LSA model. Furthermore, we implement the parallel version of our fast NN-Sparse LSA algorithm using the NVIDIA CUDA (Compute Unified Device Architecture) parallel programming framework and a data partitioning scheme that effectively reduces the memory traffic between the global memory of the Graphic Processing Unit (GPU) and the host memory. The experimental results on image classification and tag recommendation tasks on MIRFLICKR and NUS-WIDE datasets show that our parallelized fast optimization algorithm can achieve comparable or even better performance than the other examined methods, while speeds up the original optimization algorithm 20–110 times.