Parallel competitive learning algorithm for fast codebook design on partitioned space

Abstract

Vector quantization (VQ) is an attractive technique for lossy data compression, which is a key technology for data storage and/or transfer. So far, various competitive learning (CL) algorithms have been proposed to design optimal codebooks presenting quantization with minimized errors. However, their practical use has been limited for large scale problems, due to the computational complexity of competitive learning. This work presents a parallel competitive learning algorithm for fast code-book design based on space partitioning. The algorithm partitions input-vector space into some subspaces, and independently designs corresponding subcodebooks for these subspaces with computational complexity reduced. Independent processing on different subspaces can be processed in parallel without synchronization overhead, resulting in high scalability. We perform experiments of parallel codebook design on a commodity PC cluster with 8 nodes. Experimental results show that the high speedup of the codebook design is obtained without increase of quantization errors.