Shared deep kernel learning for dimensionality reduction

Abstract

Deep Kernel Learning (DKL) has been proven to be an effective method to learn complex feature representation by combining the structural properties of deep learning with the nonparametric flexibility of kernel methods, which can be naturally used for supervised dimensionality reduction. However, if limited training data are available its performance could be compromised because parameters of the deep structure embedded into the model are large and difficult to be efficiently optimized. In order to address this issue, we propose the Shared Deep Kernel Learning model by combining DKL with shared Gaussian Process Latent Variable Model. The novel method could not only bring the improved performance without increasing model complexity but also learn the hierarchical features by sharing the deep kernel. The comparison with some supervised dimensionality reduction methods and deep learning approach verify the advantages of the proposed model.