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https://hdl.handle.net/10356/146392
Title: | Novel techniques for sparse representation problems | Authors: | Chai, Woon Huei | Keywords: | Engineering::Computer science and engineering Science::Mathematics |
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. | URI: | https://hdl.handle.net/10356/146392 | 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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Thesis.pdf | 11.58 MB | Adobe PDF | ![]() View/Open |
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