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TW 381
Mike Matton, Ronald Cools
A comparison of different k-nearest neighbour algorithms with performance results on speech data
Abstract
The k-nearest neighbour problem is well known in a wide range of areas. Many algorithms to tackle this problem suffer from the ``curse of dimensionality'' which means that the execution time grows exponentially with increasing dimension. Therefore, it is important to have efficient algorithms for the problem.
In this report, some well known tree-based algorithms for the k-nearest neighbour are investigated and tested on speech data. We experimentally derive the time complexity as a function of the number of nearest neighbours k, the database size n and the bucket size b.
report.pdf (238K) / mailto: M. Matton