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Tuesday, October 26 • 10:15am - 10:30am
Co-Manifold Learning

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Technical Presentations Group 1: Algorithms, Foundations, Visualizations, and Engineering Applications

Representation learning is typically applied to only one mode of a data matrix, either its rows or columns. Yet in many applications, there is an underlying geometry to both the rows and the columns. We propose utilizing this coupled structure to perform co-manifold learning: uncovering the underlying geometry of both the rows and the columns of a given matrix. Our framework is based on computing a multiresolution view of the data at different combinations of row and column smoothness by solving a collection of continuous optimization problems. We demonstrate our method’s ability to recover the underlying row and column geometry in simulated examples and real cheminformatics data.

Authors: Eric Chi (Rice University), Gal Mishne (University of California, San Diego), and Ronald Coifman (Yale University)


Eric Chi

Rice University

Tuesday October 26, 2021 10:15am - 10:30am CDT
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