Weighting Vectors for Machine Learning: Numerical Harmonic Analysis Applied to Boundary Detection
Abstract
Metric space magnitude, an active subject of research in algebraic topology, aims to quantify the effective number of distinct points in a space. The contribution of each point to a metric space’s global magnitude, which is encoded by the {\em weighting vector}, captures much of the underlying geometry of the original metric space. When the metric space is Euclidean, the weighting vector also serves as an effective tool for boundary detection. This allows the weighting vector to serve as the foundation of novel algorithms for classic machine learning tasks such as classification, outlier detection and active learning. We demonstrate, using experiments and comparisons on classic benchmark datasets, the promise of the proposed magnitude and weighting vector-based approaches.
Cite
Text
Bunch et al. "Weighting Vectors for Machine Learning: Numerical Harmonic Analysis Applied to Boundary Detection." NeurIPS 2020 Workshops: TDA_and_Beyond, 2020.Markdown
[Bunch et al. "Weighting Vectors for Machine Learning: Numerical Harmonic Analysis Applied to Boundary Detection." NeurIPS 2020 Workshops: TDA_and_Beyond, 2020.](https://mlanthology.org/neuripsw/2020/bunch2020neuripsw-weighting/)BibTeX
@inproceedings{bunch2020neuripsw-weighting,
title = {{Weighting Vectors for Machine Learning: Numerical Harmonic Analysis Applied to Boundary Detection}},
author = {Bunch, Eric and Dickinson, Daniel and Kline, Jeffery and Fung, Glenn},
booktitle = {NeurIPS 2020 Workshops: TDA_and_Beyond},
year = {2020},
url = {https://mlanthology.org/neuripsw/2020/bunch2020neuripsw-weighting/}
}