Open access
Datum
2021-05-26Typ
- Review Article
Abstract
The last decade saw an enormous boost in the field of computational topology: methods and concepts from algebraic and differential topology, formerly confined to the realm of pure mathematics, have demonstrated their utility in numerous areas such as computational biology personalised medicine, and time-dependent data analysis, to name a few. The newly-emerging domain comprising topology-based techniques is often referred to as topological data analysis (TDA). Next to their applications in the aforementioned areas, TDA methods have also proven to be effective in supporting, enhancing, and augmenting both classical machine learning and deep learning models. In this paper, we review the state of the art of a nascent field we refer to as “topological machine learning,” i.e., the successful symbiosis of topology-based methods and machine learning algorithms, such as deep neural networks. We identify common threads, current applications, and future challenges. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000513747Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
Frontiers in Artificial IntelligenceBand
Seiten / Artikelnummer
Verlag
Frontiers MediaThema
Computational topology; Persistent homology; Machine learning; Topology; Survey; Topological machine learningFörderung
190466 - TOPAZ: Topology of Alzheimer's (SNF)