A Topological Approach to Measuring Training Data Quality
Álvaro Torras-Casas Eduardo Paluzo-Hidalgo Rocio Gonzalez-Diaz
Data quality is crucial for the successful training, generalization and
performance of artificial intelligence models. Furthermore, it is known that
the leading approaches in artificial intelligence are notoriously data-hungry.
In this paper, we propose the use of small training datasets towards faster
training. Specifically, we provide a novel topological method based on
morphisms between persistence modules to measure the training data quality with
respect to the complete dataset. This way, we can provide an explanation of why
the chosen training dataset will lead to poor performance.