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Item #:
59019550
High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications
87% of respondents would recommend this to a friend
Item #:
59019550
CLP 101093
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What Stands Out
Innovative Methodology
Presents cutting-edge techniques for analyzing complex data sets efficiently, utilizing low-dimensional models that enhance interpretability and performance.
Comprehensive Applications
Demonstrates practical applications across various fields, ensuring relevance for researchers and practitioners tackling high-dimensional data challenges in real-world scenarios.
User-Friendly Computation
Includes accessible computational guidelines and resources, empowering users to effectively implement methodologies without needing extensive programming skills.
Product Details
| Publisher | Cambridge University Press |
| Publication date | January 13, 2022 |
| Edition | New |
| Language | English |
| Print length | 716 pages |
| ISBN-10 | 1108489737 |
| ISBN-13 | 978-1108489737 |
| Item Weight | 2.31 pounds (1.05 kg) |
| Dimensions | 6.75 x 1.5 x 9.75 inches (17.1 x 3.8 x 24.8 cm) |
Who Should Buy?
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Data Scientists
Data scientists dealing with high-dimensional datasets can effectively use low-dimensional models for analysis and visualization.
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Researchers
Researchers in fields like genomics or image processing benefit from the methodologies for interpreting complex data.
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Graduate Students
Graduate students in statistics or machine learning will find valuable insights and practical applications for their studies.
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Casual Users
Individuals without a strong statistical background may struggle to grasp the advanced concepts presented in the book.
Product Description
High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications
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Computer Vision & Pattern Recognition Editorial Review
**** "High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications" is a compelling resource that stands out for its profound and insightful exploration of high-dimensional data analysis. The new edition of this work not only excels in its clear and engaging exposition of complex philosophical assumptions that underpin various algorithms but also meticulously balances rigor and intuition. This creates an enriching pathway for readers, encouraging a renaissance in both theoretical proof and practical application within learning theory. Readers have noted the book's effectiveness in transforming their understanding of the principles of intelligence, with one enthusiastic user citing its influence on achieving paper acceptances at top AI conferences and journals. The logical flow and historical context provided in this work shed light on the relevance of high-dimensional geometry to machine learning, making complex topics accessible. Furthermore, practical application examples included in the book have been found to enhance comprehension and provide real-world insights, making it a valuable tool for both novice learners and seasoned practitioners. Overall, this book appears to be a staple resource for anyone pursuing advanced studies in data analysis, particularly at the graduate level. It distinguishes itself through its approachable yet thorough content, fostering a deep understanding of high-dimensional data without overwhelming the reader. **
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Pros
- Clear exposition and balanced approach between intuition and rigor
- In-depth understanding of principles related to intelligence
- Inspiring impact on achieving academic success in AI-related fields
- Logical explanation of high-dimensional geometry’s relevance to machine learning
- Useful application examples that enhance learning experience
Cons
- The review summary does not highlight any specific Cons.
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CLP 101093
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