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Data quality is crucial for the successful training, generalization andperformance of artificial intelligence models. Furthermore, it is known thatthe leading approaches in artificial intelligence are notoriously data-hungry.In this paper, we propose the use of small training datasets towards fastertraining. Specifically, we provide a novel topological method based onmorphisms between persistence modules to measure the training data quality withrespect to the complete dataset. This way, we can provide an explanation of whythe chosen training dataset will lead to poor performance.

A Topological Approach to Measuring Training Data Quality