文献类型：会议论文

英文题名：Multiple Empirical Kernel Learning with Discriminant Locality Preservation

作者：Wang, Bolu[1];Li, Dongdong[1];Wang, Zhe[1]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China

会议论文集：11th Asian Conference on Machine Learning (ACML)

会议日期：NOV 17-19, 2019

会议地点：Nagoya, JAPAN

语种：英文

外文关键词：Multiple kernel learning; Empirical kernel mapping; Discriminant locality preserving projections; Machine learning

摘要：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 real-world datasets validate the effectiveness of the proposed MEKL-DLP compared with the classical kernel-based algorithms and state-of-art MKL algorithms.