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Abstract
Kernel methods, which are a non-linear variant of linear methods, are used to increase the flexibility and allow to examine non-linear relationships by linear methods. The conventional solution of the restricted kernel canonical correlation analysis problem has a major drawback, it solves the problem in a reasonable time frame only for problems with few variables. We successfully overcame this limitation by implementing the method with the alternating least-squares algorithm. This allowed us to apply the method to cross-language information retrieval problem on a big dataset. We compared the results to other established methods and they were encouraging.
Acknowledgments
The author would like to thank the three reviewers for their helpful comments and additional suggestions to validate the analyses, which substantially improved this paper. The author would also like to thank the Editor for their generous consideration and giving the author the opportunity to resubmit a major revision of the manuscript.