Performance Comparison of Self-Organizing Maps Based on Different Autoencoders

Boren Zheng, University of Rhode Island

Abstract

The Autoencoder (AE) is a kind of artificial neural network, which is widely used for dimensionality reduction and feature extraction in unsupervised learning tasks. Analogously, the Self-Organizing Map (SOM) is an unsupervised learning algorithm to represent the high-dimensional data by a 2D grid map, thus achieving dimensionality reduction. Some recent work has shown improvement in performance by combining the AEs with the SOMs. Knowing which variations of AEs work best and finding out whether the selection of AEs is data-depended or not is the purpose of this research. Five types of AEs are implemented in this research; three different data sets are used for training; map embedding accuracy and estimated topographic accuracy are used for measuring the model quality. Overall, this research shows that nearly all AEs at least improve the SOM performance, improving embedding accuracy and letting the training process become efficient. The Convolutional Autoencoder (ConvAE) shows an outstanding performance in image-related data set, the Denoising Autoencoder (DAE) works well with the real-word data with noise, and the Contractive Autoencoder (CAE) performs excellently in the synthetic data set. Therefore, we can see that the selection of AEs depends on the properties of data.

Subject Area

Computer science

Recommended Citation

Boren Zheng, "Performance Comparison of Self-Organizing Maps Based on Different Autoencoders" (2019). Dissertations and Master's Theses (Campus Access). Paper AAI27663762.
https://digitalcommons.uri.edu/dissertations/AAI27663762

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