Analysis and Linear Algebra [electronic resource]
This book provides an elementary analytically inclined journey to a fundamental result of linear algebra: the Singular Value Decomposition (SVD). SVD is a workhorse in many applications of linear algebra to data science. Four important applications relevant to data science are considered throughout...
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| Online Access: |
Full Text (via ProQuest) |
|---|---|
| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Providence :
American Mathematical Society,
2021.
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| Series: | Student mathematical library.
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| Subjects: |
MARC
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| 520 | |a This book provides an elementary analytically inclined journey to a fundamental result of linear algebra: the Singular Value Decomposition (SVD). SVD is a workhorse in many applications of linear algebra to data science. Four important applications relevant to data science are considered throughout the book: determining the subspace that ""best"" approximates a given set (dimension reduction of a data set); finding the ""best"" lower rank approximation of a given matrix (compression and general approximation problems); the Moore-Penrose pseudo-inverse (relevant to solving least squares problem. | ||
| 650 | 0 | |a Algebras, Linear |v Textbooks. | |
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| 650 | 0 | |a Singular value decomposition |v Textbooks. | |
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| 650 | 7 | |a Mathematical analysis. |2 fast |0 https://id.worldcat.org/fast/1012068 | |
| 650 | 7 | |a Singular value decomposition. |2 fast |0 https://id.worldcat.org/fast/1922210 | |
| 655 | 7 | |a Textbooks. |2 fast |0 https://id.worldcat.org/fast/1423863 | |
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