Optimization of Kernel Method-Based Autoencoder Using Chaotic Evolution Algorithm

Abstract

We use a chaotic evolution algorithm to optimize the parameter of Gaussian kernel function in the kernel methodbased autoencoder. Kernel method-based autoencoder is an unsupervised learning algorithm with the objective of learning a representation for a set of data. Kernel methods play an important role in building a kernel method-based autoencoder. There are some options for selecting kernel functions, such as Gaussian kernel, polynomial kernel, and Laplacian kernel, etc. In each case, we are required to identify the parameters satisfying the specified requirements or problems. Unfortunately, in some cases, because of a large range of parameters, we can not select proper parameters manually. Chaotic evolution algorithm is one of the optimization algorithms, intending to obtain optimal solutions for a problem, given its certain solution search range. We take advantage of chaotic evolution algorithm to tune parameters automatically for Gaussian kernel function in this work. We found that the proposed method is an efficient and effective tool to solve the selection issue of kernel method-based autoencoder.