Greedy embedding is a technique used in the field of machine learning and data analysis, particularly in scenarios involving optimization and representation learning. It refers to a method of creating embeddings (i.e., vector representations) of data points that aim to preserve certain relationships or structures in the data, often based on a local, greedy optimization approach.
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