Linear discriminant multiple kernel learning for multispectral image classification

In the past decade, with the development of kernel-based machine learning, many different multiple kernel learning (MKL) methods were proposed which focus on selecting the pivotal kernel to be preserved and confirming the optimal kernel combination. In this paper, we address the question mentioned above by using subspace projection method and put forward a linear discriminant based MKL (LDMKL) algorithm. LDMKL algorithm reduces the computational burden and keeps the excellent property of MKL in terms of good classification accuracy by finding the optimal projective direction which makes the intraclass scatter minimum and interclass scatter maximum instead of the time-consuming search for optimal kernel combination. Experimental results indicate that LDMKL algorithm provides the best performances among several the state-of-the-art algorithms while demonstrating satisfactory computational efficiency.