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This paper presents a three-dimensional canonical correlation analysis (TCCA) method, and applies it to feature fusion for image recognition. It is an extension of traditional canonical correlation analysis (CCA) and two-dimensional canonical correlation analysis (2DCCA). Considering two views of a three-dimensional data, the TCCA can directly find the relations between them without reshaping the data into matrices or vectors, the authors stress that TCCA dramatically reduce the computational complexity, compared to the CCA and 2DCCA.
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