Given data from a general metric space, one of the standard machine learning pipelines is to first embed the data into a Euclidean space and subsequently apply out of the box machine learning algorithms to analyze the data. The quality of such an …
In this thesis, we initiate and perform an extensive study of the theory of metric embeddings in the context of Machine Learning. We begin by asking three questions that are fundamental to any systematic study of the theory of metric embeddings. …
Created an object detection module for the system.