Cellular class encoding approach to increasing efficiency of nearest neighbor searching

Nearest neighbor searching (NNS) is a common classification method, but its brute-force (BF) implementation is inefficient for dimensions greater than 10. We present Cellular Class Encoding (CCE), shown to be 1.1–1.7 times faster than BF on real-world, 14-dimensional data sets. Moreover, if applied to bounded sets, CCE is a full-search equivalent to BF. Given a query in an indexed cell of a partitioned bounded space, the CCE's efficiency is achieved by only performing NNS on those database elements which could not be eliminated a priori as impossible nearest neighbors of that cell's resident vectors. To ensure CCE is a viable alternative in real-world applications, we use VQ speaker identification as a testbed application and present results.