Multi-View Canonical Correlation Analysis
نویسنده
چکیده
Canonical correlation analysis (CCA) is a method for finding linear relations between two multidimensional random variables. This paper presents a generalization of the method to more than two variables. The approach is highly scalable, since it scales linearly with respect to the number of training examples and number of views (standard CCA implementations yield cubic complexity). The method is also extended to handle nonlinear relations via kernel trick (this increases the complexity to quadratic complexity). The scalability is demonstrated on a large scale cross-lingual information retrieval task.
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تاریخ انتشار 2010