Structural multiple empirical kernel learning

Multiple Kernel Learning (MKL) can boost classification performance through using multiple kernels rather than a single fixed one. Unlike the traditional MKL with the implicit kernels, Multiple Empirical Kernel Learning (MEKL) explicitly maps input data into multiple feature spaces. This paper focuses on MEKL and proposes an effective Threefold Structural MEKL (TSMEKL). The first fold structure is the space structural information between different mapped feature spaces. The second one is the class discriminant information within each mapped feature space. The third one is the cluster structural information of samples in each mapped feature space. The classical MEKL mainly pays attention to the first two structures, but neglects the last one. The proposed TSMEKL introduces the cluster structural information into MEKL. Doing so can simultaneously utilize the space, the class, and the cluster information in the way from globality to locality. Therefore, TSMEKL utilizes threefold structural information to result in the improvement of classification performance. To the best of our knowledge, it is the first time to introduce the cluster information into the MEKL framework. The main advantage of the developed TSMEKL is considering different folds of data information to improve classification performance. The experimental results validate the feasibility and effectiveness of TSMEKL. Moreover, we discuss the theoretical and experimental generalization risk bound of the proposed algorithm in terms of the Rademacher complexity.