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Varshney, Lav R. Chen, Rebecca 2019-08-23T20:02:09Z 2019-08-23T20:02:09Z 2019-04-24 2019-05 In this thesis, we address several common scenarios of corrupted data in data and image processing pipelines. The first is in the setting of clustered data with missing values. We design an algorithm for imputing missing values using optimal recovery and derive an error bound for non-negative matrix factorization of the imputed data. Second, we consider missing values as erasure channels and show examples of using Fano's inequality to find lower bounds on missing values algorithms. Finally, we perform image registration of misaligned and noisy images using multiinformation and use fi nite rate of innovation sample to speed up registration while preserving optimality. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-08-22 without embargo terms The student, Rebecca Chen, accepted the attached license on 2019-04-24 at 15:11. The student, Rebecca Chen, submitted this Thesis for approval on 2019-04-24 at 15:15. This Thesis was approved for publication on 2019-04-24 at 16:57. DSpace SAF Submission Ingestion Package generated from Vireo submission #13888 on 2019-08-22 at 14:46:38 Made available in DSpace on 2019-08-23T20:02:09Z (GMT). No. of bitstreams: 2 CHEN-THESIS-2019.pdf: 2981990 bytes, checksum: 3753f0a43464e667d606cd360c0126f8 (MD5) LICENSE.txt: 4209 bytes, checksum: 1cd339952afe715b79dbd61cc5e1e3a9 (MD5) Previous issue date: 2019-04-24 application/pdf http://hdl.handle.net/2142/104929 en Copyright 2019 Rebecca Chen multiinformation, image registration non-negative matrix factorization missing values imputation Missing values imputation and image registration for genetics applications text text Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign Thesis M.S.