Latent representations of galaxy images with autoencoders
Huang, Chenyang
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https://hdl.handle.net/2142/107275
Description
Title
Latent representations of galaxy images with autoencoders
Author(s)
Huang, Chenyang
Contributor(s)
Zhao, Zhizhen
Issue Date
2020-05
Keyword(s)
data analysis
statistical method
astronomy
image processing
machine learning
Date of Ingest
2020-06-12T19:27:36Z
Abstract
This study presents a way to represent galaxy images in a low-dimension space by compressing
them into “latent variables” with Autoencoders and how this method can be used in a series of
applications. To further measure the performance of the encoding, a pipeline is set up to take a list
of measurements including MSE of the original data and the reconstruction from the latent variables,
MSE of the original label data and the recovery from the latent variables. Next, we will demonstrate
three applications of the latent variables: similarity search, outlier detection and unsupervised
clustering.
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