Information Retrieval from Incomplete Observations, Ph.D. Dissertation Sharif University of Technology ; Marvasti, Farokh (Supervisor)
Abstract
In this dissertation, Data analysis and information retrieval from incomplete observations are investigated in different applications. Incomplete observations may be induced by lack of observations or part of data affected by specific noise (quantization noise). Data-driven algorithms are among important hot topics. Our goal is to process the lost information inducing certain assumption on big data structures. Then, the approach is to mathematically model the problem of interest as an optimization problem. Next, the designed algorithms for the optimization problems are proposed trying to cut down on the computational complexity of as well as enhancing recovery accuracy for big data...
Cataloging briefInformation Retrieval from Incomplete Observations, Ph.D. Dissertation Sharif University of Technology ; Marvasti, Farokh (Supervisor)
Abstract
In this dissertation, Data analysis and information retrieval from incomplete observations are investigated in different applications. Incomplete observations may be induced by lack of observations or part of data affected by specific noise (quantization noise). Data-driven algorithms are among important hot topics. Our goal is to process the lost information inducing certain assumption on big data structures. Then, the approach is to mathematically model the problem of interest as an optimization problem. Next, the designed algorithms for the optimization problems are proposed trying to cut down on the computational complexity of as well as enhancing recovery accuracy for big data...
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