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- Tensor Computation for Data Analysis

This book is intended to provide non-specialists an overall understanding of tensor computation and its applications in data analysis and presents a systematic and up-to-date overview of tensor decompositions from the engineer's point of view, including tensor extensions of classical matrix-based data analysis techniques. It also includes practical examples in machine learning, signal processing, and other fields.
Tensor Computation for Data Analysis
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This book is intended to provide non-specialists an overall understanding of tensor computation and its applications in data analysis and presents a systematic and up-to-date overview of tensor decompositions from the engineer's point of view, including tensor extensions of classical matrix-based data analysis techniques. It also includes practical examples in machine learning, signal processing, and other fields.
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- Provides an understanding of tensor computation and its applications in data analysis for non-specialists, researchers, engineers, and students
- Offers a systematic overview of tensor decompositions and comprehensive coverage of tensor computation-based data analysis techniques
- Presents practical examples in machine learning, signal processing, data mining, computer vision, remote sensing, and biomedical engineering for easy understanding and implementation
- The book also discusses potential applications in neuroscience, communication, psychometrics, chemometrics, biometrics, quantum physics, quantum chemistry, etc.
- Covers basic notations, preliminary operations in tensor computations, main tensor decompositions and their properties, and tensor-based data analysis techniques
- Includes typical applications with experimental results such as image reconstruction, image enhancement, data fusion, signal recovery, recommendation system, knowledge graph acquisition, traffic flow prediction, and more
| Publisher | Springer |
| Publication date | September 2, 2022 |
| Edition | 2022nd |
| Language | English |
| Print length | 358 pages |
| ISBN-10 | 3030743888 |
| ISBN-13 | 978-3030743888 |
| Item Weight | 1.24 pounds (560 grams) |
| Dimensions | 6.1 x 0.82 x 9.25 inches (15.5 x 2.1 x 23.5 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to enhance their analytical skills with advanced tensor computation techniques.
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Graduate Students
Beneficial for graduate students studying data analysis or machine learning, providing essential theoretical foundations and practical applications.
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AI Researchers
Great for AI researchers needing to implement tensor operations efficiently in their machine learning algorithms.
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Beginners
Not suitable for beginners lacking foundational knowledge in data analysis or tensor mathematics.
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Tensor Computation for Data Analysis
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ventajas
- Comprehensive coverage of tensors
- User-friendly explanations
- Great for data analysis techniques
- Includes practical examples
- Well-structured and organized
Contras
- Some topics may require prior knowledge.
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características y beneficios
- Provides an overall understanding of tensor computation and its applications in data analysis.
- Presents a systematic and up-to-date overview of tensor decompositions from an engineer's point of view.
- Includes practical examples in machine learning, signal processing, and other fields.
- Covers tensor dictionary learning, low rank tensor recovery, tensor completion, coupled tensor analysis, tensor regression, and others.
- Additionally, this book includes a number of typical applications with experimental results.
- The practical data analysis techniques presented can be applied in a wide range of applications such as neuroscience, quantum chemistry, and more.
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