Multiple random projection for fast, approximate nearest neighbor search in high dimensions.
Published: 2004
Language: eng
Number of pages: 84
Description: Random Projection has recently been used as a promising dimensionality reduction technique. Using random projection can speed up the finding of approximate nearest neighbors (NN) but it can't easily be used for exact NN. On the other hand, k-d tree and other related data structures can find exact NN, but as the dimensionality of the feature space increases these structures become quickly inefficient. The computational cost of these tree data structures grow almost exponentially with the intrinsic dimensionality of the data. In this thesis, we present experimental results evaluating the performance of exact and approximate methods for NN search on a variety of real and synthetic data sets. Finally, we present a hybrid model of Multiple Random Projection (MRP) and k-d tree to find approximate nearest neighbors in high dimension. The experimental results show that this hybridization results in improved performance w.r.t. number of distance calculations needed to find NN.
Notes: Adviser:  Sam Roweis.

Thesis (M.Sc.)--University of Toronto, 2004.

Electronic version licensed for access by U. of T. users.

Source: Masters Abstracts International, Volume: 43-03, page: 0869.

MICR copy on microfiche (1 microfiche).