Abstract
One important process of Latent semantic analysis (LSA) is the weighting scheme to the term-document matrix by weight function, the weight function has directly affects the quality of LSA. This text leads to apriori information and global weighting of document on the traditional method and the modified weight function base on Fuzzy membership, Calculation of Latent Semantic Weight Based on Fuzzy Membership is proposed in this paper. By the last experiment, the results show that Latent Semantic Analysis based on modified weight function is better than that old one. The experiments show the expected results obtained, and the feasibility and advantage of the new spam filtering method is validated.
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Sun, J., Zhang, Q., Yuan, Z., Huang, W., Yan, X., Dong, J. (2008). Calculation of Latent Semantic Weight Based on Fuzzy Membership. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_11
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DOI: https://doi.org/10.1007/978-3-540-87734-9_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87733-2
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