FALCON: A Fast Drop-In Replacement of Citation KNN for Multiple Instance Learning

Shuai Yang, Xipeng Shen

Abstract: Citation KNN is an important but compute-intensive algorithm for multiple instance learning (MIL). This paper presents FALCON, a fast replacement of Citation KNN. FALCON accelerates Citation KNN by removing unnecessary distance calculations through two novel optimizations, multi-level triangle inequality-based distance filtering and heap optimization. The careful design allows it to produce the same results as the original Citation KNN does while avoiding 84--99.8% distance calculations. On seven datasets of various sizes and dimensions, FALCON consistently outperforms Citation KNN by one or two orders of magnitude, making it a promising drop-in replacement of Citation KNN for multiple instance learning.