An Efficient Approximation-elimination Algorithm for Fast Nearest-neighbor Search

In this paper, we present an efficient algorithm for fast nearest-neighbour search in multidimensional space under a so called approximation-elimination framework. The algorithm is based on a new approximation procedure which selects codevectors for distance computation in the close proximity of the test vector and eliminates codevectors using the triangle inequality based elimination. The algorithm is studied in the context of vector quantization of speech and compared with related algorithms proposed earlier. It is shown to be more efficient in terms of reducing the main search complexity, overhead costs and storage.