Soft kernel target alignment for two-stage multiple kernel learning
Published in International Conference on Discovery Science, 2016
Huibin Shen, Sandor Szedmak, Céline Brouard, Juho Rousu. (2016). "Soft kernel target alignment for two-stage multiple kernel learning" International Conference on Discovery Science https://link.springer.com/chapter/10.1007/978-3-319-46307-0_27
Abstract
The two-stage multiple kernel learning (MKL) algorithms gained the popularity due to their simplicity and modularity. In this paper, we focus on two recently proposed two-stage MKL algorithms: ALIGNF and TSMKL. We first show through a simple vectorization of the input and target kernels that ALIGNF corresponds to a non-negative least squares and TSMKL to a non-negative SVM in the transformed space. Then we propose ALIGNF+, a soft version of ALIGNF, based on the observation that the dual problem of ALIGNF is essentially a one-class SVM problem. It turns out that the ALIGNF+ just requires an upper bound on the kernel weights of original ALIGNF. This upper bound makes ALIGNF+ interpolate between ALIGNF and the uniform combination of kernels. Our experiments demonstrate favorable performance and improved robustness of ALIGNF+ comparing to ALIGNF.