Scientific talk by David Loiseaux
Topological Persistence for Machine Learning
Abstract:
The development of data science and data acquisition technologies in the industry in the last decade led to the emergence of enormous datasets. In the context of Machine Learning, this induces several challenges both on the theoretical side, and on the practical side. On one hand, the so-called curse of dimensionality prevents the construction of usual statistics directly from such datasets, and on the other hand, the size of these datasets constraints the complexity of algorithms that we can use. Topological Data Analysis (TDA) aims at proposing solutions to these issues for geometrical datasets, by computing concise and interpretable geometric features that can be used afterward along with various machine learning techniques, such as classification, statistical regularization, clustering, visualization. Surprisingly, several general machine learning problems and datasets, ranging from time series to medical images, can be framed as geometrical questions. This wide range of applications has highlighted the usefulness of TDA tools, which attracted a lot of attention over the last years. In this talk, I will introduce the main tool of TDA, Persistent Homology, as well as its generalization called Multiparameter Persistent Homology.
When: Monday, October 21 at 2pm
Where: Euler Violet
