Latent Semantic Indexing is an unsupervised feature extraction technology, and its effectiveness has been proven in several research fields such as information indexing.Because the effect relies entirely on the characteristic distribution of data, optimizing the data can improve the technology′s effectiveness.An optimized technology of the Latent Semantic Indexing-Augmented Space Model has been proposed, and a new strategy based on the documents′ lengths and distribution of the features′ DF is also presented in this paper, which can ensure that the favorable structure of big scale corpus can be inherited by the two subspaces as far as possible.Experiments prove that precision and a shorter time of the algorithm can be obtained by an appropriate subspace dividing strategy.In the end, this paper shows a better performance-the precision in the classification experiment is 85.92%-by adopting the Augmented Space Model to integrate different subspaces.
JI Duo, CHANG Li-wei, CAI Dong-feng
. Research of Latent Semantic Indexing based on subspace optimization[J]. Journal of Shenyang Aerospace University, 2013
, 30(2)
: 60
-65
.
DOI: 10.3969/j.issn.2095-1248.2013.02.014
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