Nonparametrically Guided Autoencoder with Laplace Approximation for dimensionality reduction
Unsupervised learning aims to discovery latent representation embedded in the observation, which is useful for data visualization, dimensionality reduction, and density modeling. Autoencoders have been successfully used to learn the latent variations in data, especially with the recent reintroduction by deep learning. For some specific tasks, there are supervised information or labels that can be used to further guide the unsupervised autoencoder model for finding latent representation. The Non Parametrically Guided Autoencoder (NPGA) has been proved to be an effective model. It tries to utilize Gaussian Process Regression (GPR) to model the unknown mapping from unknown latent representation to extra supervised information. However for the discrete label information in classification tasks, using GPR could be unwise and inefficient. In this paper, we propose the Non Parametrically Guided Autoencoder with Laplace Approximation (NPGA LA) to effectively handle discrete labels. The idea of NPGA LA is to make use of Gaussian Process Classification (GPC) rather than GPR to model the transformation between the latent space and the discrete label space. The experimental results verify the excellent performance of the newly developed method.
2016 International Joint Conference on Neural Networks (IJCNN)