Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patrec.2012.05.002
Title: Memory-restricted latent semantic analysis to accumulate term-document co-occurrence events
Authors: Na, S.-H. 
Lee, J.-H.
Keywords: Co-occurrence
Dimensionality reduction
Latent semantic analysis
Partial-update algorithm
Issue Date: 2012
Source: Na, S.-H., Lee, J.-H. (2012). Memory-restricted latent semantic analysis to accumulate term-document co-occurrence events. Pattern Recognition Letters 33 (12) : 1623-1631. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patrec.2012.05.002
Abstract: This paper addresses a novel adaptive problem of obtaining a new type of term-document weight. In our problem, an input is given by a long sequence of co-occurrence events between terms and documents, namely, a stream of term-document co-occurrence events. Given a stream of term-document co-occurrences, we learn unknown latent vectors of terms and documents such that their inner product adaptively approximates the target query-based term-document weights resulting from accumulating co-occurrence events. To this end, we propose a new incremental dimensionality reduction algorithm for adaptively learning a latent semantic index of terms and documents over a collection. The core of our algorithm is its partial updating style, where only a small number of latent vectors are modified for each term-document co-occurrence, while most other latent vectors remain unchanged. Experimental results on small and large standard test collections demonstrate that the proposed algorithm can stably learn the latent semantic index of terms and documents, showing an improvement in the retrieval performance over the baseline method. © 2012 Elsevier B.V. All rights reserved.
Source Title: Pattern Recognition Letters
URI: http://scholarbank.nus.edu.sg/handle/10635/39069
ISSN: 01678655
DOI: 10.1016/j.patrec.2012.05.002
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