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Using KCCA for Japanese-English cross-language information retrieval and classification AbstractKernel Canonical Correlation Analysis (KCCA) is a method of correlating linear relationship between two variables in a kernel dened 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 signicantly 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 classication using KCCA as well as other methods. Our results show that it is feasible to use a classier learned in one language to classify the documents in other languages.
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