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Improvement of information filtering by independent components selection
Author(s) -
Yokoi Takeru,
Yanagimoto Hidekazu,
Omatu Sigeru
Publication year - 2008
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.20519
Subject(s) - sort , computer science , divergence (linguistics) , imputation (statistics) , selection (genetic algorithm) , relevance (law) , data mining , process (computing) , information retrieval , independent component analysis , artificial intelligence , machine learning , missing data , linguistics , philosophy , political science , law , operating system
We propose an improvement of an information filtering process with independent components selection. The independent components are obtained by Independent Components Analysis and considered as topics. Selection of independent components is an efficient method of improving the accuracy of the information filtering for the purpose of extraction of similar topics by focusing on their meanings. To achieve this, we select the topics by Maximum Distance Algorithm with Jensen‐Shannon divergence. In addition, document vectors are represented by the selected topics. We create a user profile from transformed data with a relevance feedback. Finally, we sort documents by the user profile and evaluate the accuracy by imputation precision. We carried out an evaluation experiment to confirm the validity of the proposed method considering meanings of components used in this experiment. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 163(2): 49–56, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20519