A Binary Classification Framework for Two-Stage Multiple Kernel Learning
This work addresses the challenge of making MKL more scalable and accessible for practitioners, though it is incremental in its approach.
The paper tackles the problem of automating kernel specification in Multiple Kernel Learning (MKL) by framing it as a binary classification problem with constraints, resulting in a method that performs comparably to leading MKL approaches on nine datasets.
With the advent of kernel methods, automating the task of specifying a suitable kernel has become increasingly important. In this context, the Multiple Kernel Learning (MKL) problem of finding a combination of pre-specified base kernels that is suitable for the task at hand has received significant attention from researchers. In this paper we show that Multiple Kernel Learning can be framed as a standard binary classification problem with additional constraints that ensure the positive definiteness of the learned kernel. Framing MKL in this way has the distinct advantage that it makes it easy to leverage the extensive research in binary classification to develop better performing and more scalable MKL algorithms that are conceptually simpler, and, arguably, more accessible to practitioners. Experiments on nine data sets from different domains show that, despite its simplicity, the proposed technique compares favorably with current leading MKL approaches.