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CITATION.cff
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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
The Classification of Optical Galaxy Morphology Using
Unsupervised Learning Techniques
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Ezra
family-names: Fielding
email: [email protected]
affiliation: University of the Western Cape
orcid: 'https://orcid.org/0000-0002-7936-0222'
- given-names: Clement N.
family-names: Nyirenda
email: [email protected]
affiliation: University of the Western Cape
orcid: 'https://orcid.org/0000-0002-4181-0478'
- given-names: Mattia
family-names: Vaccari
email: [email protected]
affiliation: University of the Western Cape
orcid: 'https://orcid.org/0000-0002-6748-0577'
identifiers:
- type: doi
value: 10.1109/ICECET55527.2022.9872611
description: The DOI of the work.
- type: doi
value: 10.48550/arXiv.2206.06165
description: The ArXiv deposit of the encompassing paper.
repository-code: 'https://github.com/ezrafielding/galaxy-cluster'
url: 'https://sites.google.com/myuwc.ac.za/galaxy-classification'
abstract: >-
In recent years, large scale data intensive astronomical
surveys have resulted in more detailed images being
produced than scientists can manually classify. Even
attempts to crowd-source this work will soon be outpaced
by the large amount of data generated by modern surveys.
This has brought into question the viability of
human-based methods for classifying galaxy morphology.
While supervised learning methods require datasets with
existing labels, unsupervised learning techniques do not.
Therefore, this paper implements unsupervised learning
techniques to classify the Galaxy Zoo DECaLS dataset. A
convolutional autoencoder feature extractor was trained
and implemented. The resulting features were then
clustered via k-means, fuzzy c-means and agglomerative
clustering. These clusters were compared against the true
volunteer classifications provided by the Galaxy Zoo
DECaLS project. The best results, in general, were
produced by the agglomerate clustering method. However,
the increase in performance compared to k-means clustering
was not significant considering the increase in clustering
time. After undergoing the appropriate clustering
algorithm optimizations, this approach could prove useful
for classifying the better performing questions and could
serve as the basis for a novel approach to generating more
"human-like" galaxy morphology classifications from
unsupervised techniques.
license: MIT
date-released: '2022-07-22'
preferred-citation:
type: conference-paper
authors:
- given-names: Ezra
family-names: Fielding
email: [email protected]
affiliation: University of the Western Cape
orcid: 'https://orcid.org/0000-0002-7936-0222'
- given-names: Clement N.
family-names: Nyirenda
email: [email protected]
affiliation: University of the Western Cape
orcid: 'https://orcid.org/0000-0002-4181-0478'
- given-names: Mattia
family-names: Vaccari
email: [email protected]
affiliation: University of the Western Cape
orcid: 'https://orcid.org/0000-0002-6748-0577'
title: The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques
doi: 10.1109/ICECET55527.2022.9872611
pages: 1-6
year: '2022'
conference:
name: 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET)
date-start: "2022-07-20"
date-end: "2022-07-22"