Compressed Sensing: Mathematical Formulation
This video introduces the mathematical theory of compressed sensing, related to high-dimensional geometry, robust statistics, and optimization.
Book Website: http://databookuw.com
Book PDF: http://databookuw.com/databook.pdf
These lectures follow Chapter 3 from:
"Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Amazon: https://www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical/dp/1108422098/
Brunton Website: eigensteve.com
Видео Compressed Sensing: Mathematical Formulation канала Steve Brunton
Book Website: http://databookuw.com
Book PDF: http://databookuw.com/databook.pdf
These lectures follow Chapter 3 from:
"Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Amazon: https://www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical/dp/1108422098/
Brunton Website: eigensteve.com
Видео Compressed Sensing: Mathematical Formulation канала Steve Brunton
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Compressed Sensing: When It WorksWavelets and Multiresolution AnalysisSparsity and the L1 NormCompressed Sensing for Magnetic Resonance - Understand the technologyRobust Principal Component Analysis (RPCA)Shannon Nyquist Sampling TheoremHankel Alternative View of Koopman (HAVOK) Analysis [FULL]The Laplace Transform: A Generalized Fourier TransformWhy images are compressible: The Vastness of Image SpaceCompressive SensingSingular Value Decomposition (SVD): Mathematical OverviewCompressed Sensing: OverviewDeep Learning of Dynamics and Coordinates with SINDy AutoencodersRobust, Interpretable Statistical Models: Sparse Regression with the LASSOThe Spectrogram and the Gabor TransformImage Compression with the FFT (Examples in Matlab)Emmanuel Candès: Wavelets, sparsity and its consequencesNeural Network Architectures & Deep LearningDynamic Mode Decomposition (Overview)