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Title: Novel techniques for sparse representation problems
Authors: Chai, Woon Huei
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Chai, W. H. (2021). Novel techniques for sparse representation problems. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Sparse representations have been used in solving many problems in computer science. Two issues that need to be addressed in formulating such a representation are: the problem design; and the optimization technique. Many optimization problems contain one/multiple non-smooth terms in the objective function. Besides, the feasibility of an optimization problem depends on the availability of adequate computational resources. In this thesis, a new parallelizable optimization technique that uses more information and has better convergence than state-of-the-art counterparts is presented. Theoretical derivation of the bound of the recovery probability of using sparse representation based on a L_1-minimization is also shown. A clustering-based technique for dictionary and signal dimension reduction to replace the traditional naïve downsampling technique is introduced to address computational resource constraints. Finally, an anomaly detection and localization technique using a sparse representation problem and used in a case study for an important and challenging field; namely automated visual inspection (AVI) is presented. The experimental results are encouraging.
DOI: 10.32657/10356/146392
Schools: Interdisciplinary Graduate School (IGS) 
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IGS Theses

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