Compressed Sensing and Its Applications

Third International MATHEON Conference 2017

Holger Boche, Giuseppe Caire, Robert Calderbank, Gitta Kutyniok, Rudolf Mathar, Philipp Petersen (Herausgeber)

Buch | Hardcover

XVII, 295 Seiten

2019 | 1st ed. 2019
Springer International Publishing (Verlag)
978-3-319-73073-8 (ISBN)

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The chapters in this volume highlight the state-of-the-art of compressed sensing and are based on talks given at the third international MATHEON conference on the same topic, held from December 4-8, 2017 at the Technical University in Berlin. In addition to methods in compressed sensing, chapters provide insights into cutting edge applications of deep learning in data science, highlighting the overlapping ideas and methods that connect the fields of compressed sensing and deep learning. Specific topics covered include:

Quantized compressed sensing
Classification
Machine learning
Oracle inequalities
Non-convex optimization
Image reconstruction
Statistical learning theory

This volume will be a valuable resource for graduate students and researchers in the areas of mathematics, computer science, and engineering, as well as other applied scientists exploring potential applications of compressed sensing.

An Introduction to Compressed Sensing.- Quantized Compressed Sensing: a Survey.- On reconstructing functions from binary measurements.- Classification scheme for binary data with extensions.- Generalization Error in Deep Learning.- Deep learning for trivial inverse problems.- Oracle inequalities for local and global empirical risk minimizers.- Median-Truncated Gradient Descent: A Robust and Scalable Nonconvex Approach for Signal Estimation.- Reconstruction Methods in THz Single-pixel Imaging.

Erscheinungsdatum	22.04.2019
Reihe/Serie	Applied and Numerical Harmonic Analysis
Zusatzinfo	XVII, 295 p. 57 illus., 39 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	621 g
Themenwelt	Mathematik / Informatik ► Informatik ► Theorie / Studium
Themenwelt	Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
Schlagworte	compressed sensing • Compressed sensing 2019 • Compressed sensing book • Compressed sensing introduction • Compressed sensing theory and applications • deep learning book • Deep learning compressed sensing • dimensionality reduction • Fourier phase retrieval • Generalization error machine learning • Hilbert spaces • Information and Communication, Circuits • machine learning • MATHEON conference • Quantized compressed sensing • random matrices • Signal sensing book • sparse approximation • sparse probability measures • stochastic block model
ISBN-10	3-319-73073-8 / 3319730738
ISBN-13	978-3-319-73073-8 / 9783319730738
Zustand	Neuware