NDLI: Latent semantic analysis for multiple-type interrelated data objects

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Latent semantic analysis for multiple-type interrelated data objects

Content Provider	ACM Digital Library
Author	Sun, Jian-Tao Wang, Xuanhui Chen, Zheng Zhai, ChengXiang
Abstract	Co-occurrence data is quite common in many real applications. Latent Semantic Analysis (LSA) has been successfully used to identify semantic relations in such data. However, LSA can only handle a single co-occurrence relationship between two types of objects. In practical applications, there are many cases where multiple types of objects exist and any pair of these objects could have a pairwise co-occurrence relation. All these co-occurrence relations can be exploited to alleviate data sparseness or to represent objects more meaningfully. In this paper, we propose a novel algorithm, M-LSA, which conducts latent semantic analysis by incorporating all pairwise co-occurrences among multiple types of objects. Based on the mutual reinforcement principle, M-LSA identifies the most salient concepts among the co-occurrence data and represents all the objects in a unified semantic space. M-LSA is general and we show that several variants of LSA are special cases of our algorithm. Experiment results show that M-LSA outperforms LSA on multiple applications, including collaborative filtering, text clustering, and text categorization.
Starting Page	236
Ending Page	243
Page Count	8
File Format	PDF
ISBN	1595933697
DOI	10.1145/1148170.1148214
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-08-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Mutual reinforcement principle M-lsa Lsa Multiple-type
Content Type	Text
Resource Type	Article

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