Files in this item

FilesDescriptionFormat

application/pdf

application/pdfSP20-ECE499-Thesis-Huang, Chenyang.pdf (1MB)
(no description provided)PDF

Description

Title:Latent representations of galaxy images with autoencoders
Author(s):Huang, Chenyang
Contributor(s):Zhao, Zhizhen
Subject(s):data analysis
statistical method
astronomy
image processing
machine learning
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.
Issue Date:2020-05
Genre:Other
Type:Text
Language:English
URI:http://hdl.handle.net/2142/107275
Date Available in IDEALS:2020-06-12


This item appears in the following Collection(s)

Item Statistics