A Novel Multiple Kernel Learning Framework for Multiple Feature Classification

Multiple Kernel Learning (MKL) algorithms have recently demonstrated their effectiveness for classifying the data with numerous features. These algorithms aim at learning an optimal composite kernel through combining the basis kernels constructed from different features. Despite their satisfactory results, MKL algorithms assume that the basis kernels are a priori computed. Moreover, they adopt complex optimization methods to train the combination of the basis kernels, which are usually hard to solve and can only handle the binary classification problems. In this paper, a novel MKL framework was introduced in order to address all these issues. This framework optimizes a data-dependent kernel evaluation measure in order to learn both the basis kernels and their combination. The kernel evaluation measure should be able to estimate the goodness of the composite kernel for a multiclass classification problem. In this paper, we defined such a measure based on the similarity between the composite kernel and an ideal kernel. To this end, three different kernel-based similarity measures, namely kernel alignment (KA), centered kernel alignment (CKA), and Hilbert-Schmidt independence criterion (HSIC), were presented. For solving the optimization problem of the proposed MKL framework, we used the metaheuristic optimization algorithms, which in addition to being accurate algorithms can be easily implemented. The performance of the proposed framework was evaluated by classifying the features extracted from two multispectral and hyperspectral datasets. The results showed that this framework outperformed the other state-of-the-art MKL algorithms in terms of both classification accuracy and the computational time.