Deep Kernelized Autoencoders
Conference paper
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Abstract
In this paper we introduce the deep kernelized autoencoder, a neural network model that allows an explicit approximation of (i) the mapping from an input space to an arbitrary, user-specified kernel space and (ii) the back-projection from such a kernel space to input space. The proposed method is based on traditional autoencoders and is trained through a new unsupervised loss function. During training, we optimize both the reconstruction accuracy of input samples and the alignment between a kernel matrix given as prior and the inner products of the hidden representations computed by the autoencoder. Kernel alignment provides control over the hidden representation learned by the autoencoder. Experiments have been performed to evaluate both reconstruction and kernel alignment performance. Additionally, we applied our method to emulate kPCA on a denoising task obtaining promising results.
Keywords
Autoencoders Kernel methods Deep learning Representation learningNotes
Acknowledgments
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research. This work was partially funded by the Norwegian Research Council FRIPRO grant no. 239844 on developing the Next Generation Learning Machines.
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