---
title: "Soft kernel target alignment for two-stage multiple kernel learning"
collection: publications
permalink: /publication/2016-10-19-Soft
date: 2016-10-19
venue: 'International Conference on Discovery Science'
paperurl: 'https://link.springer.com/chapter/10.1007/978-3-319-46307-0_27'
citation: '<b><NAME></b>, <NAME>, <NAME>, <NAME>. (2016). &quot;Soft kernel target alignment for two-stage multiple kernel learning&quot; <i>International Conference on Discovery Science</i>'
---

[Link](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.