Algebraic topology in machine learning
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EmailFacebookTwitter Topological Based Machine Learning Methods2019
...Main ContentMetricsAuthor & Article Info Abstract This dissertation presents novel approaches and applications of machine learning architectures. In particular, these approaches are based on tools from topological data analysis and are used in conjunction with conventional machine learning methods. Topological data analysis, which is based on algebraic topology, can identify significant global mathematical structures which are out of reach of many other approaches. When we use topology we benefit from generality, and when we use conventional methods we benefit from specificity. Show
This dissertation contains a broad overview of data science and topological data analysis, then transitions to three distinct machine learning applications of these methods. The first application uses linear methods to discover the inherent dimensionality of the manifold given by congressional roll call votes. The second uses persistent homology to identify extremely noisy images in both supervised and unsupervised tasks. The last application uses mapper objects to produce robust classification algorithms. Two additional projects are presented later in the appendix, and are related to the three main applications. The first of these constructs a method to choose optimal optimizers, and the second places mathematical constraints on the structure of renormalization group flows. Download PDF to ViewView Larger For improved accessibility of PDF content, download the file to your device. Thumbnails Document Outline Attachments PreviousNext Highlight all Match case Whole words Presentation Mode Open Print Download Current ViewGo to First Page Go to Last PageRotate Clockwise Rotate CounterclockwiseText Selection Tool Hand ToolVertical Scrolling Horizontal Scrolling Wrapped ScrollingNo Spreads Odd Spreads Even SpreadsDocument Properties Toggle SidebarFind PreviousNext Presentation Mode Open Print Download Current ViewTools Zoom OutZoom In More Information Less Information Close Enter the password to open this PDF file: Cancel OK File name: - File size: - Title: - Author: - Subject: - Keywords: - Creation Date: - Modification Date: - Creator: - PDF Producer: - PDF Version: - Page Count: - Page Size: - Fast Web View: - Close Preparing document for printing 0% Cancel Jump ToArticle
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