  In this paper, we propose a novel supervised learning method that is called
Deep Embedding Kernel (DEK). DEK combines the advantages of deep learning and
kernel methods in a unified framework. More specifically, DEK is a learnable
kernel represented by a newly designed deep architecture. Compared with
pre-defined kernels, this kernel can be explicitly trained to map data to an
optimized high-level feature space where data may have favorable features
toward the application. Compared with typical deep learning using SoftMax or
logistic regression as the top layer, DEK is expected to be more generalizable
to new data. Experimental results show that DEK has superior performance than
typical machine learning methods in identity detection, classification,
regression, dimension reduction, and transfer learning.
