Bilinear Discriminant Component Analysis
Mads Dyrholm, Christoforos Christoforou, Lucas C. Parra; 8(39):1097−1111, 2007.
Abstract
Factor analysis and discriminant analysis are often used as complementary approaches to identify linear components in two dimensional data arrays. For three dimensional arrays, which may organize data in dimensions such as space, time, and trials, the opportunity arises to combine these two approaches. A new method, Bilinear Discriminant Component Analysis (BDCA), is derived and demonstrated in the context of functional brain imaging data for which it seems ideally suited. The work suggests to identify a subspace projection which optimally separates classes while ensuring that each dimension in this space captures an independent contribution to the discrimination.
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