- Session 3: Supervised and General Machine Learning -- Day 3 (Nov.19), talks: 10:50-11:30 (5th floor Hall 1), poster session: 11:30-14:00
- Poster number: Tue02
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Bolu Wang (East China University of Science and Technology); Dongdong Li (East China University of Science and Technology); Zhe Wang ( East China University of Science and Technology )
Multiple Kernel Learning (MKL) algorithm effectively combines different kernels to improve the performance of classification. Most MKL algorithms implicitly map samples into feature space by the form of inner-product. In contrast, Multiple Empirical Kernel Learning (MEKL) can explicitly map the input spaces into feature spaces so that the mapped feature vectors are explicitly represented, which is easy to process and analyze the adaptability of kernels for input space. Meanwhile, in order to pay attention to the structure and discriminant information of samples in empirical feature space, inspired by discriminant locality preserving projections, we introduce the discriminant locality preservation regularization into MEKL framework to propose the Multiple Empirical Kernel Learning with Discriminant Locality Preservation (MEKL-DLP). Experiments conducted on realworld datasets validate the effectiveness of the proposed MEKL-DLP compared with the classical kernel-based algorithms and state-of-art MKL algorithms.