@article{luo_generalized_2020, abstract = {Topological Data Analysis ({TDA}) provides novel approaches that allow us to analyze the geometrical shapes and topological structures of a dataset. As one important application, {TDA} can be used for data visualization and dimension reduction. We follow the framework of circular coordinate representation, which allows us to perform dimension reduction and visualization for high-dimensional datasets on a torus using persistent cohomology. In this paper, 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. We provide simulation experiments and real data analysis to support our claim that circular coordinates with generalized penalty will accommodate the sparsity in high-dimensional datasets under different sampling schemes while preserving the topological structures.}, author = {Luo, Hengrui and Patania, Alice and Kim, Jisu and Vejdemo-Johansson, Mikael}, date = {2020-06-03}, eprint = {2006.02554}, eprinttype = {arxiv}, journaltitle = {{arXiv}:2006.02554 [cs, math, stat]}, keywords = {1 - High-dimensional data, 1 - Machine learning, 1 - Non-linear dimension reduction, 1 - Sonar, 1 - Voting, 2 - Persistent cohomology}, title = {Generalized Penalty for Circular Coordinate Representation}, url = {http://arxiv.org/abs/2006.02554}, urldate = {2020-06-05} }