Kernelized Linear Autoencoder

摘要

This work proposes a new representation learning model called kernelized linear autoencoder. Instead of modeling non-linearity by the non-linear activation functions, we employ linear activations but account for non-linearity by the kernel trick. We propose four variants. The first one is the basic unsupervised kernelized linear autoencoder. The second one is a label consistent version for classification. The third one incorporates sparse sub-space clustering into the autoencoder formulation. Finally, we proposed a coupled version for domain adaptation problems. For each task, we compare our proposed technique with shallow and deep representation learning methods on benchmark problems. For each task, we perform significantly better than the methods compared against.