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JIIS
2006

Using KCCA for Japanese-English cross-language information retrieval and document classification

13 years 7 months ago
Using KCCA for Japanese-English cross-language information retrieval and document classification
Kernel Canonical Correlation Analysis (KCCA) is a method of correlating linear relationship between two variables in a kernel defined feature space. A machine learning algorithm based on KCCA is studied for cross-language information retrieval. We apply the algorithm in Japanese-English cross-language information retrieval. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. Computational complexity is an important issue when applying KCCA to large dataset as in information retrieval. We experimentally evaluate several methods to alleviate the problem of applying KCCA to large datasets. We also investigate cross-language document classification using KCCA as well as other methods. Our results show that it is feasible to use a classifier learned in one language to classify the documents in other languages.
Yaoyong Li, John Shawe-Taylor
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where JIIS
Authors Yaoyong Li, John Shawe-Taylor
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