View Proposal #527
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| ID | 527 |
|---|---|
| First Name | Hongyuan |
| Last Name | Zhang |
| Institution | Grinnell College |
| Speaker Category | undergraduate student |
| Title of Talk | Artworks and Articles Meet Mapper and Persistent Homology |
| Abstract | Since its recent birth, topological data analysis (TDA) has proven to be a very useful tool when studying large and high-dimensional data sets. We will talk about the application of two TDA tools, persistent homology and the Mapper algorithm, to the Metropolitan Museum of Art (MET) artwork data set and two scholarly literature databases: arXiv and Google Scholar. For the MET data, we use the Mapper Algorithm to guide feature selection in building a logistic regression model for classifying certain artworks. Then we use persistent homology to help differentiate between two subsets of artwork. For the arXiv data, we use persistent homology to derive a general sense of the shape of the data. With help of the Mapper Algorithm, we further explore the point cloud by analyzing trends and features in visualizations. For the Google Scholar data, we find that there are interesting correlations between academic category of the paper and number of pages, number of references, and published date. |
| Subject area(s) | applied topology |
| Suitable for undergraduates? | Y |
| Day Preference | Either |
| Computer Needed? | Y |
| Bringing a laptop? | Y |
| Overhead Needed? | Y |
| Software requests | Microsoft Powerpoint |
| Special Needs | |
| Date Submitted | 08/30/2019 |
| Year | 2019 |