arXiv:1812.09245
공식 코드 · Papers with Code
Persistence Bag-of-Words for Topological Data Analysis
원문 초록
Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs). PDs exhibit, however, complex structure and are difficult to integrate in today's machine learning workflows. This paper introduces persistence bag-of-words: a novel and stable vectorized representation of PDs that enables the seamless integration with machine learning. Comprehensive experiments show that the new representation achieves state-of-the-art performance and beyond in much less time than alternative approaches.
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Comparing the Effects of Persistence Barcodes Aggregation and Feature Concatenation on Medical Imaging
Topological Machine Learning for Protein-Nucleic Acid Binding Affinity Changes Upon Mutation
Topology across Scales on Heterogeneous Cell Data
Short-PHD: Detecting Short LLM-generated Text with Topological Data Analysis After Off-topic Content Insertion
A Topological Data Analysis Framework for Quantifying Necrosis in Glioblastomas
Persistent Homology-induced Graph Ensembles for Time Series Regressions
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