Skip to Main Content
In this letter, a new manifold learning algorithm, called uncorrelated discriminant locality preserving projections (UDLPP), is proposed. The aim of UDLPP is to preserve the within-class geometric structure, while maximizing the between-class distance. By introducing a simple uncorrelated constraint into the objective function, we show that the extracted features via UDLPP are statistically uncorrelated, which is desirable for many pattern analysis applications. Moreover, UDLPP can be performed in reproducing kernel Hilbert space, which gives rise to kernel UDLPP. Experimental results on both face recognition and radar target recognition demonstrate the effectiveness of the proposed algorithm.