Accelerating Nyström Kernel Independent Component Analysis with Many Integrated Core Architecture

Kernel independent component analysis (KICA) penalizes the correlations among components in a reproducing kernel Hilbert space (RKHS) and performs well in many practical tasks such as speech separation due to its robustness on varying source distributions. Recently, Nystrom-KICA (NKICA) incorporates a low-rank approximation and low-complexity sampling method to reduce the computational complexity of KICA. In this paper, we show that the computational complexity of NKICA can be further decreased by implementing the algorithm on the many integrated core (MIC) architecture to meet the requirement of large data processing. Particularly, we parallelize the critical segments with the OpenMP technology and perform the intensive matrix manipulations on a MIC coprocessor. This MIC-based approach has been evaluated on both simulated dataset and the TIMIT dataset. The experimental results confirm the efficiency of our implementation of NKICA on the MIC architecture, and show that it achieves a consistent speedup rate of around 10 on average, and of 12.3 at best, comparing with that performed on single CPU.