Principal Component Analysis Based Filtering for Scalable, High Precision k-NN Search.

Approximate $k$<alternatives> <inline-graphic xlink:href="eyers-ieq1-2748131.gif"/></alternatives> Nearest Neighbours (A $k$<alternatives> <inline-graphic xlink:href="eyers-ieq2-2748131.gif"/></alternatives>NN) search is widely used in domains such as computer vision and machine learning. However, A$k$ <alternatives><inline-graphic xlink:href="eyers-ieq3-2748131.gif"/></alternatives>NN search in high-dimensional datasets does not scale well on multicore platforms, due to its large memory footprint. Parallel A $k$<alternatives> <inline-graphic xlink:href="eyers-ieq4-2748131.gif"/></alternatives>NN search using space subdivision for filtering helps reduce the memory footprint, but its loss of precision is unstable. In this paper, we propose a new data filtering method—PCAF—for parallel A$k$ <alternatives><inline-graphic xlink:href="eyers-ieq5-2748131.gif"/></alternatives>NN search based on principal component analysis. PCAF improves on previous methods, demonstrating sustained, high scalability for a wide range of high-dimensional datasets on both Intel and AMD multicore platforms. Moreover, PCAF maintains highly precise A$k$<alternatives> <inline-graphic xlink:href="eyers-ieq6-2748131.gif"/></alternatives>NN search results. [ABSTRACT FROM AUTHOR]