Circular Coordinate Methods with Generalized Penalty Functions

Anonymous

Keywords: Topological data analysis, persistent cohomology, high-dimensional data, non-linear dimension reduction

Abstract: The circular coordinate representation performs dimension reduction and visualization for high-dimensional datasets on a torus using persistent cohomology. In this work, we propose a method to adapt the circular coordinate framework to take into account sparsity in high-dimensional applications. We use a generalized penalty function instead of an $L_{2}$ penalty in the traditional circular coordinate algorithm.

Previous Submission: Yes