From bits to information : learning meets compressive sensing
Name
912309122-MIT.pdf
Description
Full printable version
Size
46.99 MB
Format
Adobe PDF
Checksum (MD5)
20721d4f6c69faef6ccfc0877761ed1d
Author(s)
Mroueh, Youssef
Advisor(s)
Tomaso Poggio and Lorenzo Rosasco.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
A quantization approach to supervised learning, compressive sensing, and phase retrieval is presented in this thesis. We introduce a set of common techniques that allow us, in those three settings, to represent high dimensional data using the order statistics of linear and non linear measurements. We introduce new algorithms for signals classification in the multiclass and the multimodal settings, as well as algorithms for signals representation and recovery from quantized linear and quadratic measurements. We analyze the statistical consistency of our algorithms and prove their robustness to different sources of perturbation, as well as their computational efficiency. We present and analyze applications of our theoretical results in realistic setups, such as computer vision classification tasks, Audio-Visual Automatic Speech Recognition, lossy image compression and retrieval via locality sensitive hashing, locally linear estimation in large scale learning and Fourier sampling for phase retrieval - of particular interest in X-ray crystallography and super-resolution diffraction imaging applications. Our analysis of quantization based algorithms highlights interesting tradeoffs between memory complexity, sample complexity, and time complexity in algorithms design.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 174-182).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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