A supervised method for nonlinear dimensionality reduction with GPLVM

Dimensionality reduction is a very important task in many artificial applications. Among the current researches, Gaussian process latent variable model (GPLVM), which is a nonlinear dimensionality reduction method, has become a hot topic in recent years. In this paper, a supervised version of GPLVM with the prior characterized by the determinantal clustering process (DCP) prior is proposed. The combination of GPLVM with the DCP prior over the low dimensional positions has the ability of preservation the discriminative property between different classes. Experiments were conducted on two data sets, and the results demonstrated the better performance of the proposed method with respect to the original GPLVM.