Topological methods for unsupervised learning. (English) Zbl 1458.68174

McInnes, Leland

Nielsen, Frank (ed.) et al., Geometric science of information. 4th international conference, GSI 2019, Toulouse, France, August 27–29, 2019. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 11712, 343-350 (2019).

Summary: Unsupervised learning is a broad topic in machine learning with many diverse sub-disciplines. Within the field of unsupervised learning we will consider three major topics: dimension reduction; clustering; and anomaly detection. We seek to use the languages of topology and category theory to provide a unified mathematical approach to these three major problems in unsupervised learning.
For the entire collection see [Zbl 1428.94016].

MSC:

68T05	Learning and adaptive systems in artificial intelligence
62H30	Classification and discrimination; cluster analysis (statistical aspects)

Keywords:

unsupervised learning; manifold learning; clustering