Novel techniques for sparse representation problems
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 f...
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| 格式: | Thesis-Doctor of Philosophy |
| 語言: | English |
| 出版: |
Nanyang Technological University
2021
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/146392 |
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| 總結: | 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. |
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