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http://hdl.handle.net/11375/25538
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DC Field | Value | Language |
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dc.contributor.advisor | McNicholas, Sharon M. | - |
dc.contributor.advisor | Nicas, Andrew J. | - |
dc.contributor.author | Tan, Anthony | - |
dc.date.accessioned | 2020-07-17T19:13:27Z | - |
dc.date.available | 2020-07-17T19:13:27Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://hdl.handle.net/11375/25538 | - |
dc.description.abstract | 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. | en_US |
dc.language.iso | en | en_US |
dc.subject | Algebraic Topology | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Applied Topology | en_US |
dc.subject | Persistent Homology | en_US |
dc.title | Persistent Homology and Machine Learning | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Mathematics and Statistics | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Science (MSc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Tan_Anthony_FinalSubmission202006_MScStats.pdf | 999.91 kB | Adobe PDF | View/Open |
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