A New Classification Algorithm Based on Gaussian Process Latent Variable Model
Wang Xiu · Chinese Journal of Computers · 2012
Gaussian process latent variable model is a new probabilistic approach for dimensionality reduction.It can obtain a low-dimensional manifold of a data set in an entirely unsupervised way.However,when there is some supervised information in the data set,Gaussian process latent variable model cannot use this information for supervised tasks,e.g.,classification and regression learning.For this purpose,a supervised Gaussian process latent variable model for classification is developed.The maximum-a-posterior algorithm is employed to estimate all latent variables position.Compared with the traditional Gaussian process latent variable model,the supervised version of this model shows more advantages in experiments.