A manifold framework of multiple-kernel learning for hyperspectral image classification

Abstract

Manifold learning is a promising intelligent data analysis method, and the manifold learning preserves the local embedding features of the data in manifold mapping space. Manifold learning has its limitations on extracting the nonlinear features of the data in many applications. For example, hyperspectral image classification needs to seek the nonlinear local relationships between spectral curves. For that, researchers applied the kernel trick to manifold learning in the previous works. The kernel-based manifold learning was developed, but still endures the problem that the inappropriate kernel model reduces the system performance. In order to solve the problem of kernel model selection, we propose a manifold framework of multiple-kernel learning for the application of hyperspectral image classification. In this framework, the quasiconformal mapping-based multiple-kernel model is optimized based on the optimization objective equation, which maximizes the class discriminant ability of data. Accordingly, the discriminative structure of data distribution is achieved for classification with the quasiconformal mapping-based multiple-kernel model.