Knihu momentálně nemáme skladem

Linear Algebra for Pattern Processing
Projection, Singular Value Decomposition, and Pseudoinverse
Autoři
Více o knize
Focusing on geometric interpretations, this book bridges linear algebra with pattern information processing, particularly in analyzing high-dimensional data relevant to computer vision and graphics. It covers essential concepts like projection, spectral decomposition, and singular value decomposition, illustrating their applications in least-squares solutions and covariance matrices. The text emphasizes visualizing abstract spaces and includes practical examples, such as reconstructing 3D locations from camera views, to enhance understanding of linear algebra's role in data analysis amidst noise.
Nákup knihy
Linear Algebra for Pattern Processing, Kenichi Kanatani
- Jazyk
- Rok vydání
- 2021
- product-detail.submit-box.info.binding
- (měkká)
Jakmile ji vyčmucháme, pošleme vám e-mail.
Doručení
Platební metody
Navrhnout úpravu
- Titul
- Linear Algebra for Pattern Processing
- Podtitul
- Projection, Singular Value Decomposition, and Pseudoinverse
- Jazyk
- anglicky
- Autoři
- Kenichi Kanatani
- Vydavatel
- Springer International Publishing
- Rok vydání
- 2021
- Vazba
- měkká
- Počet stran
- 156
- ISBN13
- 9783031014161
- Kategorie
- Technika / Strojírenství, Elektrotechnika
- Anotace
- Focusing on geometric interpretations, this book bridges linear algebra with pattern information processing, particularly in analyzing high-dimensional data relevant to computer vision and graphics. It covers essential concepts like projection, spectral decomposition, and singular value decomposition, illustrating their applications in least-squares solutions and covariance matrices. The text emphasizes visualizing abstract spaces and includes practical examples, such as reconstructing 3D locations from camera views, to enhance understanding of linear algebra's role in data analysis amidst noise.