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68308841
Generalized Principal Component Analysis (Interdisciplinary Applied Mathematics, 40)
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Item #:
68308841
XCD 253
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Comprehensive introduction to latest advances in mathematical theory and computational tools for modeling high-dimensional data.
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What Stands Out
Interdisciplinary Focus
Integrates concepts from various fields, making it essential for researchers seeking a broader understanding of Mathematical applications across disciplines.
Advanced Methodology
Presents a sophisticated approach to data analysis, equipping users with innovative tools to tackle complex datasets beyond traditional methods.
Practical Applications
Equips readers with real-world examples and applications, ensuring concepts are directly applicable to current challenges in applied mathematics and related fields.
Product Details
- Comprehensive introduction to mathematical theory and computational tools for modeling high-dimensional data
- Focuses on one or multiple low-dimensional subspaces and methods for estimation and segmentation
- Includes real-world applications in image processing, face recognition, and system identification
- Intended as a textbook for graduate students and beginning researchers in data science, machine learning, and related fields
- Contains ample illustrations, examples, exercises, and three Appendices with basic concepts and principles
- Written by René Vidal, Yi Ma, and S. Shankar Sastry
| Publisher | Springer |
| Publication date | April 14, 2018 |
| Edition | Softcover reprint of the original 1st ed. 2016 |
| Language | English |
| Print length | 598 pages |
| ISBN-10 | 1493979124 |
| ISBN-13 | 978-1493979127 |
| Item Weight | 1.83 pounds (830 grams) |
| Dimensions | 6.1 x 1.36 x 9.25 inches (15.5 x 3.5 x 23.5 cm) |
| Part of series | Interdisciplinary Applied Mathematics |
Product Description
Generalized Principal Component Analysis (Interdisciplinary Applied Mathematics, 40)
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Features & Benefits
- Introduces latest advances in mathematical theory of modeling high-dimensional data.
- Covers new algebraic, geometric, statistical, and computational methods
- Includes real-world applications in image processing, face recognition, clustering and hybrid system identification
- Contains illustrations, examples and exercises
- Ideal for graduate students and researchers in data science, machine learning and image processing
- Made largely self-contained with three appendices surveying basic concepts and principles
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