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In this paper, we present a convex discriminant analysis formulation, which is extended to solve multi-label classification problems. The original Linear Discriminant Analysis energy optimization function is turned into another form as a convex formulation (namely, convex Approximate LDA, denoted as “convexALDA” for short) using the generalized eigen-decomposition. We give applications by incorporating convexALDA as a regularizer into discriminant regression analysis. Extensive experimental results on multi-label classification tasks and an extensive application scenario on communication characteristics of imperial examination system are provided. In this way we have a brand-new comprehension for it, and a new idea and method was also put forward for studying the system.

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