Supervised kernel neighborhood preserving projections for radar target recognition

Dimensionality reduction is a crucial step for pattern recognition tasks and finding a suitable low-dimensional subspace has an important effect on recognition performance. Recently, the computer vision and pattern recognition community has witnessed the rapid growth of a new kind of dimensionality reduction method, manifold learning. Among them, neighborhood preserving projections (NPP) is one of the most promising techniques. Based on NPP, we propose a novel nonlinear dimensionality reduction algorithm, called supervised kernel neighborhood preserving projections (SKNPP), which aims at preserving the local manifold structures defined by within-class samples in some high-dimensional feature space. SKNPP can not only gain a perfect nonlinear approximation of data manifold through kernel technique, but also enhance the local within-class relations by taking into account class label information. The proposed SKNPP is compared with NPP, LDA and KLDA on radar target recognition with range profiles. Experimental results indicate the promising recognition performance of the proposed method.