Generalized Penalty for Circular Coordinate Representation
This work addresses a domain-specific problem for researchers in topological data analysis, offering an incremental improvement to existing methods.
The authors tackled the problem of adapting circular coordinate representation in Topological Data Analysis to handle roughness in change-point and high-dimensional applications by using a generalized penalty function instead of an L2 penalty, resulting in improved detection of changes in high-dimensional datasets while preserving topological structures, as supported by simulation experiments and real data analysis.
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 the roughness of circular coordinates in change-point and 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 detect the change in high-dimensional datasets under different sampling schemes while preserving the topological structures.