In Stock
Tensor decompositions for data science
Hardback
€79.74
Collect 239 Reward Points
- Free Delivery from
- This Book Is Available Online Only
- Book Synopsis
- Tensors are essential in modern day computational and data sciences. This book explores the foundations of tensor decompositions, a data analysis methodology that is ubiquitous in machine learning, signal processing, chemometrics, neuroscience, quantum computing, financial analysis, social science, business market analysis, image processing, and much more. In this self-contained mathematical, algorithmic, and computational treatment of tensor decomposition, the book emphasizes examples using real-world downloadable open-source datasets to ground the abstract concepts. Methodologies for 3-way tensors (the simplest notation) are presented before generalizing to d-way tensors (the most general but complex notation), making the book accessible to advanced undergraduate and graduate students in mathematics, computer science, statistics, engineering, and physical and life sciences. Additionally, extensive background materials in linear algebra, optimization, probability, and statistics are included as appendices.
- About The Author
- Grey Ballard is Associate Professor of Computer Science at Wake Forest University. He specializes in numerical linear algebra, high performance computing, and computational science, with much of his work focusing on numerical methods and software for tensor decompositions. His work has been recognized with a National Science Foundation (NSF) Faculty Early Career Development (CAREER) award, a SIAM Linear Algebra Best Paper Prize, and conference best paper awards at the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), IEEE International Parallel & Distributed Processing Symposium (IPDPS), and IEEE International Conference on Data Mining (ICDM).
- Product Details
-
- ISBN
- 9781009471671
- Format
- Hardback
- Publisher
- Cambridge University Press, (26 June 2025)
- Number of Pages
- 419
- Weight
- 1060 grams
- Language
- English
- Dimensions
- 260 x 181 x 27 mm
- Categories: