Autoencoder using kernel methoc
Yan Pei · 2017
We propose a method that uses kernel method-based algorithms to implement an autoencoder. Deep learning-based algorithms have two characteristics, one is the high level data abstraction, the other is the multiple level data transformations and representations. The kernel method is one of the approaches that can be used in linear and non-linear transformations. It should be one of the implementations of these transformations in the deep learning. In this paper, the encoder part and decoder part of the autoencoder are implemented by kernel-based principal component analysis and kernel-based linear regression, respectively. As autoencoder is a basic structure and algorithm in deep learning, the proposed method can implement deep learning model and algorithm using duplicate structures. We use image data to evaluate our proposed method. The results show that kernel-based autoencoder can represent and restore image data, but the performance depends on the kernel function and its parameters' selection. We also discuss and analyse some open topics and works towards a study of kernel method-based deep learning.