ISBN: 9781680838886 Minimum-Distortion Embedding (Foundations and Trends(r) in Machine Learning) (1 items)

Now Publishers, 2021 8vo (23.5 cm). X, 174 pp. Laminated wrappers. "Embeddings provide concrete numerical representations of otherwise abstract items, for use in downstream tasks. For example, a biologist might look for subfamilies of related cells by clustering embedding vectors associated with individual cells, while a machine learning practitioner might use vector representations of words as features for a classification task. In this monograph the authors present a general framework for faithful embedding called minimum-distortion embedding (MDE) that generalizes the common cases in which similarities between items are described by weights or distances. The MDE framework is simple but general. It includes a wide variety of specific embedding methods, including spectral embedding, principal component analysis, multidimensional scaling, Euclidean distance problems, etc. The authors provide a detailed description of minimum-distortion embedding problem and describe the theory behind creating… Read more