Image classification via least square semi-supervised discriminant analysis with flexible kernel regression for out-of-sample extension

Semi-supervised dimensionality reduction is an important research topic in many pattern recognition and machine learning applications. Among all the methods for semi-supervised dimensionality reduction, SDA and LapRLS/L are two popular ones. Though the two methods are actually the extensions of different supervised methods, we show in this paper that both SDA and Lap-RLS/L can be unified under a regularized least square framework. In this paper, we propose a new effective semi-supervised dimensionality reduction method for better cope with data sampled from nonlinear manifold. In addition, the proposed method can both handle the regression as well as the subspace learning problem. Theoretical analysis and extensive simulations show the effectiveness of our algorithm. The results in simulations demonstrate that our proposed algorithm can achieve great superiority compared with other existing methods.