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Parallel Monte Carlo algorithms for information retrieval

Alexandrov, V. N., Dimov, I. T., Karaivanova, A. and Tan, C. J. K. (2003) Parallel Monte Carlo algorithms for information retrieval. Mathematics and Computers in Simulation, 62 (3-6). pp. 289-295. ISSN 0378-4754

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Abstract/Summary

In any data mining applications, automated text and text and image retrieval of information is needed. This becomes essential with the growth of the Internet and digital libraries. Our approach is based on the latent semantic indexing (LSI) and the corresponding term-by-document matrix suggested by Berry and his co-authors. Instead of using deterministic methods to find the required number of first "k" singular triplets, we propose a stochastic approach. First, we use Monte Carlo method to sample and to build much smaller size term-by-document matrix (e.g. we build k x k matrix) from where we then find the first "k" triplets using standard deterministic methods. Second, we investigate how we can reduce the problem to finding the "k"-largest eigenvalues using parallel Monte Carlo methods. We apply these methods to the initial matrix and also to the reduced one. The algorithms are running on a cluster of workstations under MPI and results of the experiments arising in textual retrieval of Web documents as well as comparison of the stochastic methods proposed are presented. (C) 2003 IMACS. Published by Elsevier Science B.V. All rights reserved.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:15102
Uncontrolled Keywords:singular value decomposition, stochastic methods, data mining, Lanczos, method, eigenvalue computation

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