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Information Retrieval from Incomplete Observations

Esmaeili, Ashkan | 2019

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 51911 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Marvasti, Farokh
  7. Abstract:
  8. 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 applications. Compressive Sensing (CS) and Matrix Completion (MC) are widely used in modeling computational methods, Signal processing, and Machine Learning. In many applications, sparse signals or low0rank matrices are taken into account which are not fully sensed or observed. In this manuscript, we first introduce a CS approach which yields noticeable reconstruction quality in low sampling scenarios of CS. In following chapters, diverse MC applications are considered, and proposed algorithms for big data are investigated compared to literature algorithms
  9. Keywords:
  10. Compressive Sensing ; Matrix Completion ; Semi-Supervised Learning ; Incomplete Observations ; Quantized Matrix Completion ; Sparse Data Recovery ; Low-Rank Matrix

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