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In this paper, we propose the problem of optimizing multivariate performancemeasures from multi-view data, and an effective method to solve it. Thisproblem has two features: the data points are presented by multiple views, andthe target of learning is to optimize complex multivariate performancemeasures. We propose to learn a linear discriminant functions for each view,and combine them to construct a overall multivariate mapping function formult-view data. To learn the parameters of the linear dis- criminant functionsof different views to optimize multivariate performance measures, we formulatea optimization problem. In this problem, we propose to minimize the complexityof the linear discriminant functions of each view, encourage the consistencesof the responses of different views over the same data points, and minimize theupper boundary of a given multivariate performance measure. To optimize thisproblem, we employ the cutting-plane method in an iterative algorithm. In eachiteration, we update a set of constrains, and optimize the mapping functionparameter of each view one by one.

Multi-view learning for multivariate performance measures optimization