Missing values imputation and image registration for genetics applications
Chen, Rebecca
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CHEN-THESIS-2019.pdf
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https://hdl.handle.net/2142/104929
Description
Title
Missing values imputation and image registration for genetics applications
Author(s)
Chen, Rebecca
Issue Date
2019-04-24
Director of Research (if dissertation) or Advisor (if thesis)
Varshney, Lav R.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2019-08-23T20:02:09Z
Keyword(s)
multiinformation, image registration
non-negative matrix factorization
missing values
imputation
Abstract
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.
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CHEN-THESIS-2019.pdf
