Minimum-Distortion Embedding (Foundations and Trends(r) in Machine Learning)
de Agrawal, Akshay; Ali, Alnur; Boyd, Stephen
- Nuevo
- Tapa blanda
- Estado
- New
- ISBN 10
- 1680838881
- ISBN 13
- 9781680838886
- Librería
-
Kraków, Poland
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Sobre este artículo
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 solutions to all aspects. They also give describe in detail algorithms for computing minimum-distortion embeddings. Finally, they provide examples on how to approximately solve many MDE problems involving real datasets, including images, co-authorship networks, United States county demographics, population genetics, and single-cell mRNA transcriptomes. An accompanying open-source software package, PyMDE, makes it easy for practitioners to experiment with different embeddings via different choices of distortion functions and constraint sets. The theory and techniques described and illustrated in this book will be of interest to researchers and practitioners working on modern-day systems that look to adopt cutting-edge artificial intelligence." (publisher's description)
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Detalles
- Librería
- Leopolis Volodymyr Dmyterko (PL)
- Inventario del vendedor #
- 008553
- Título
- Minimum-Distortion Embedding (Foundations and Trends(r) in Machine Learning)
- Autor
- Agrawal, Akshay; Ali, Alnur; Boyd, Stephen
- Formato/Encuadernación
- Soft cover
- Estado del libro
- Nuevo New
- Cantidad disponible
- 2
- Encuadernación
- Tapa blanda
- ISBN 10
- 1680838881
- ISBN 13
- 9781680838886
- Editorial
- Now Publishers
- Fecha de publicación
- 2021
- Catálogos del vendedor
- Mathematics;
Términos de venta
Leopolis Volodymyr Dmyterko
Any book may be returned within 14 days for any reason. All books remain our property until paid for in full. Export of books, manuscripts, maps etc. is subject to the Act of 23 July 2003 on the protection of arts and antiquities. Orders usually ship within 3 business days via priority or express mail, with tracking.
Sobre el vendedor
Leopolis Volodymyr Dmyterko
Miembro de Biblio desde 2019
Kraków
Sobre Leopolis Volodymyr Dmyterko
Specializing in mathematical sciences, Church Slavonic and Slavic books, Eastern European history, travels and topography, bibliography, history and art of the book, history of libraries and collections
Glosario
Algunos términos que podrían usarse en esta descripción incluyen:
- Wrappers
- The paper covering on the outside of a paperback. Also see the entry for pictorial wraps, color illustrated coverings for...