Persistence Codebooks for Topological Data Analysis
This provides a practical solution for researchers and practitioners in machine learning and data science to efficiently apply topological data analysis to heterogeneous datasets.
The paper tackles the challenge of integrating variable-size persistence diagrams from topological data analysis into standard machine learning workflows by introducing persistence codebooks, a fixed-size vector representation that achieves state-of-the-art performance with reduced computational time.
Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs) which are 2D multisets of points. Their variable size makes them, however, difficult to combine with typical machine learning workflows. In this paper we introduce persistence codebooks, a novel expressive and discriminative fixed-size vectorized representation of PDs. To this end, we adapt bag-of-words (BoW), vectors of locally aggregated descriptors (VLAD) and Fischer vectors (FV) for the quantization of PDs. Persistence codebooks represent PDs in a convenient way for machine learning and statistical analysis and have a number of favorable practical and theoretical properties including 1-Wasserstein stability. We evaluate the presented representations on several heterogeneous datasets and show their (high) discriminative power. Our approach achieves state-of-the-art performance and beyond in much less time than alternative approaches.