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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/582E1023-0632-417C-ABC8-F073FA7C2B81 | - |
| Identifier | https://doi.org/10.26233/heallink.tuc.79106 | - |
| Language | en | - |
| Extent | 77 pages | el |
| Title | Greedy algorithms for reconstruction of high-dimensional sparse vectors | en |
| Title | “ ‘Απληστοι” αλγόριθμοι μηχανικής μάθησης για την ανακατασκευή αραιών διανυσμάτων πολύ μεγάλης διάστασης | el |
| Creator | Siaminou Ioanna | en |
| Creator | Σιαμινου Ιωαννα | el |
| Contributor [Thesis Supervisor] | Liavas Athanasios | en |
| Contributor [Thesis Supervisor] | Λιαβας Αθανασιος | el |
| Contributor [Committee Member] | Karystinos Georgios | en |
| Contributor [Committee Member] | Καρυστινος Γεωργιος | el |
| Contributor [Committee Member] | Lagoudakis Michail | en |
| Contributor [Committee Member] | Λαγουδακης Μιχαηλ | el |
| Publisher | Πολυτεχνείο Κρήτης | el |
| Publisher | Technical University of Crete | en |
| Academic Unit | Technical University of Crete::School of Electrical and Computer Engineering | en |
| Academic Unit | Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών | el |
| Content Summary | Reconstruction of signals from measured data is often encountered in various fields of science. However, the dimension of the target signal is often much larger than the number of the collected measurements. In these cases, signal reconstruction is practically impossible in general. Luckily, by assuming that the signal we wish to reconstruct has certain structure, the reconstruction becomes feasible. In Compressed Sensing, we deal with the system y = Ax, where the so-called measurement matrix A has dimensions (m x n), with m < n. In this area, the notion of sparsity is used as a constraint on the target signal x. In this thesis, we concentrate on greedy algorithms, studied extensively in the literature, and the conditions that guarantee successful reconstruction. First, we provide a theoretical background of Compressed Sensing and, afterwards, we proceed with the presentation and analysis of greedy algorithms, such as Orthogonal Matching Pursuit (OMP) and Compressive Sampling Matching Pursuit (CoSaMP). We complement our presentation with numerical experiments, using as performance metric the relative signal reconstruction error. Then, we investigate the extension of sparse vector reconstruction in non-linear scenarios. For this purpose, we consider a greedy algorithm, the Gradient Support Pursuit (GraSP), which is an extension of CoSaMP. We present the conditions that must be satisfied in this framework for successful reconstruction, and compare the performance of GraSP to LASSO, of the GLMnet package, for the logistic model. Finally, we propose a method for non-linear scenarios inspired by GraSP and OMP, test it for the logistic model, and compare the results to those of GraSP and GLMnet. | en |
| Type of Item | Διπλωματική Εργασία | el |
| Type of Item | Diploma Work | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2018-10-11 | - |
| Date of Publication | 2018 | - |
| Subject | Compressed sensing | en |
| Subject | Sparse recovery | en |
| Bibliographic Citation | Ioanna Siaminou, "Greedy algorithms for reconstruction of high-dimensional sparse vectors", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2018 | en |
| Bibliographic Citation | Ιωάννα Σιάμινου, "“ ‘Απληστοι” αλγόριθμοι μηχανικής μάθησης για την ανακατασκευή αραιών διανυσμάτων πολύ μεγάλης διάστασης", Διπλωματική Εργασία, Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2018 | el |
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