FKNDT: A Flexible Kernel by Negotiating Between Data-dependent Kernel Learning and Task-dependent Kernel Learning

Kernel learning is a challenging issue which has been vastly investigated over the last decades. The performance of kernel-based methods broadly relies on selecting an appropriate kernel. In machine learning community, a fundamental problem is how to model a suitable kernel. The traditional kernels, e.g., Gaussian kernel and polynomial kernel, are not adequately flexible to employ the information of the given data. Classical kernels are unable to sufficiently depict the characteristics of data similarities. To alleviate this problem, this paper presents a Flexible Kernel by Negotiating between Data-dependent kernel learning and Task-dependent kernel learning termed as FKNDT. Our method learns a suitable kernel by way of the Hadamard product of two types of kernels; a data-dependent kernel and a set of pre-specified classical kernels as a task-dependent kernel. We evaluate a flexible kernel in a supervised manner via Support Vector Machines (SVM). We model a learning process as a joint optimization problem including data-dependent kernel matrix learning, multiple kernel learning by means of quadratic programming, and standard SVM optimization. The experimental results demonstrate our technique provides a more effective kernel than the traditional kernels. Our method is better than other state-of-the-art kernel-based algorithms in terms of classification accuracy on fifteen benchmark datasets.