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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.
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Linear Algebra for Pattern Processing, Kenichi Kanatani
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- Année de publication
- 2021
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