Improving the Speed of Kernel PCA on Large Scale Datasets

This paper concerns making large scale Kernel Principal Component Analysis (KPCA) feasible on regular hardware. The KPCA has been proven a useful non-linear feature extractor in several computer vision applications. The standard computation method for KPCA, however, scales badly with the problem size, thus limiting the potential of the technique for large scale data. We propose a novel method to alleviate this problem. The essence of our solution lies in partitioning the data and greedily filtering each partition for a sparse representation. Incremental KPCA is then utilized to merge each partition to arrive at the overall KPCA. We also provide experimental results which demonstrate the effectiveness of the approach.