Unembedding
Process of reversing the transformation of data from its original high-dimensional space to a lower-dimensional space.
Unembedding refers to the procedure of transitioning data that has been previously embedded, from a lower-dimensional representation to its original, or a similar, higher-dimensional space. This technique is often used in complex machine learning applications where data dimensionality reduction techniques, such as embedding, have been applied to simplify the dataset or algorithm inputs. Unembedding may be necessary when one wants to analyze or visualize the data in its original context, after it has undergone a series of transformations for the purposes of ML model optimization.
The conceptual foundations of unembedding originate from the broader concept of 'embedding', used commonly in mathematics and computer science. The idea of reverting this process, i.e., unembedding, particularly in the context of machine learning and AI, gained traction with the advent of advanced dimensionality reduction techniques during the early part of the 21st century, most notably in conjunction with deep learning algorithms.
The application and development of unembedding concepts in AI is a collective effort of the community of AI researchers and scientists. It is difficult to attribute this to specific individuals as it is an integral counterpart to the well-established process of embedding. However, Geoffrey Hinton, a pioneer in deep learning, has significantly contributed to the broader understanding of data transformations, including embedding and unembedding.