Persistent homology is a technique of topological data analysis that seeks to understand
the shape of data. We study the effectiveness of a single-layer perceptron and gradient
boosted classification trees in classifying perhaps the most well-known data set in machine learning, the MNIST-Digits, or MNIST. An alternative representation is constructed, called MNIST-PD. This construction captures the topology of the digits using persistence diagrams, a product of persistent homology. We show that the models are more effective when trained on MNIST compared to MNIST-PD. Promising evidence reveals that the topology is learned by
the algorithms. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/25538 |
| Date | January 2020 |
| Creators | Tan, Anthony |
| Contributors | McNicholas, Sharon M., Nicas, Andrew J., Mathematics and Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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