Speaker
Koketso Mohale
(University of the Western Cape)
Description
Telescopes such as the Square Kilometre Array (SKA) and Vera C. Rubin Observatory (LSSRT) will produce more data than astronomers can analyse manually. Machine learning, being data-driven, is increasingly being applied in astronomy. Unsupervised machine learning, in particular, is a powerful approach for finding patterns and anomalies automatically, but struggles with high-dimensional data like images. We explore an approach for reducing the dimensionality of the data, called representation learning. We also present novel model-independent methods for measuring the utility of these representations for supervised learning.
| Stream | Science or Engineering |
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Primary author
Koketso Mohale
(University of the Western Cape)
Co-author
Prof.
Michelle Lochner
(University of the Western Cape)