2020/10/10 by Polina Pilyugina, Pilyugina, Polina, Rodrigo Rivera-Castro +2
Computer Science · Medicine · #Clusterin in disease pathology #FOS: Computer and information sciences #Machine Learning (cs.LG) #Topological and Geometric Data Analysis
paper · pdf · doi:10.48550/arxiv.2010.05056
openalex publication_date 2020/10/10 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
This work is devoted to a comprehensive analysis of topological data analysis\nfortime series classification. Previous works have significant shortcomings,\nsuch aslack of large-scale benchmarking or missing state-of-the-art methods. In\nthis work,we propose TOTOPO for extracting topological descriptors from\ndifferent types ofpersistence diagrams. The results suggest that TOTOPO\nsignificantly outperformsexisting baselines in terms of accuracy. TOTOPO is\nalso competitive with thestate-of-the-art, being the best on 20% of univariate\nand 40% of multivariate timeseries datasets. This work validates the hypothesis\nthat TDA-based approaches arerobust to small perturbations in data and are\nuseful for cases where periodicity andshape help discriminate between classes.\n