Affiliation:
1. School of Electronics and Computer Science, University of Southampton, University Road, Southampton SO17 1BJ, UK
Abstract
Recent research has suggested that category theory can provide useful insights into the field of machine learning (ML). One example is improving the connection between an ML problem and the design of a corresponding ML algorithm. A tool from category theory called a Kan extension is used to derive the design of an unsupervised anomaly detection algorithm for a commonly used benchmark, the Occupancy dataset. Achieving an accuracy of 93.5% and an ROCAUC of 0.98, the performance of this algorithm is compared to state-of-the-art anomaly detection algorithms tested on the Occupancy dataset. These initial results demonstrate that category theory can offer new perspectives with which to attack problems, particularly in making more direct connections between the solutions and the problem’s structure.
Funder
Engineering and Physical Sciences Research Council (EP-SRC), UK, and Senseye
Reference16 articles.
1. Shiebler, D., Gavranović, B., and Wilson, P. (2021). Category Theory in Machine Learning. arXiv.
2. Shiebler, D. (2022). Kan Extensions in Data Science and Machine Learning. arXiv.
3. Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models;Candanedo;Energy Build.,2016
4. Riehl, E. (2016). Category Theory in Context, Dover Publications Inc.
5. Fong, B., and Spivak, D.I. (2018). Seven Sketches in Compositionality: An Invitation to Applied Category Theory. arXiv.