Multi-dimensional model order selection
<p>Abstract</p> <p>Multi-dimensional model order selection (MOS) techniques achieve an improved accuracy, reliability, and robustness, since they consider all dimensions jointly during the estimation of parameters. Additionally, from fundamental identifiability results of multi-dim...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2011-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://asp.eurasipjournals.com/content/2011/1/26 |
| _version_ | 1818934107392966656 |
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| author | Roemer Florian Haardt Martin da Costa João de Sousa Rafael |
| author_facet | Roemer Florian Haardt Martin da Costa João de Sousa Rafael |
| author_sort | Roemer Florian |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Multi-dimensional model order selection (MOS) techniques achieve an improved accuracy, reliability, and robustness, since they consider all dimensions jointly during the estimation of parameters. Additionally, from fundamental identifiability results of multi-dimensional decompositions, it is known that the number of main components can be larger when compared to matrix-based decompositions. In this article, we show how to use tensor calculus to extend matrix-based MOS schemes and we also present our proposed multi-dimensional model order selection scheme based on the closed-form PARAFAC algorithm, which is only applicable to multi-dimensional data. In general, as shown by means of simulations, the Probability of correct Detection (PoD) of our proposed multi-dimensional MOS schemes is much better than the PoD of matrix-based schemes.</p> |
| first_indexed | 2024-12-20T04:59:01Z |
| format | Article |
| id | doaj.art-3421b9bd3df6493ea714b3bc4458ff99 |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-20T04:59:01Z |
| publishDate | 2011-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-3421b9bd3df6493ea714b3bc4458ff992022-12-21T19:52:36ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802011-01-012011126Multi-dimensional model order selectionRoemer FlorianHaardt Martinda Costa Joãode Sousa Rafael<p>Abstract</p> <p>Multi-dimensional model order selection (MOS) techniques achieve an improved accuracy, reliability, and robustness, since they consider all dimensions jointly during the estimation of parameters. Additionally, from fundamental identifiability results of multi-dimensional decompositions, it is known that the number of main components can be larger when compared to matrix-based decompositions. In this article, we show how to use tensor calculus to extend matrix-based MOS schemes and we also present our proposed multi-dimensional model order selection scheme based on the closed-form PARAFAC algorithm, which is only applicable to multi-dimensional data. In general, as shown by means of simulations, the Probability of correct Detection (PoD) of our proposed multi-dimensional MOS schemes is much better than the PoD of matrix-based schemes.</p>http://asp.eurasipjournals.com/content/2011/1/26 |
| spellingShingle | Roemer Florian Haardt Martin da Costa João de Sousa Rafael Multi-dimensional model order selection EURASIP Journal on Advances in Signal Processing |
| title | Multi-dimensional model order selection |
| title_full | Multi-dimensional model order selection |
| title_fullStr | Multi-dimensional model order selection |
| title_full_unstemmed | Multi-dimensional model order selection |
| title_short | Multi-dimensional model order selection |
| title_sort | multi dimensional model order selection |
| url | http://asp.eurasipjournals.com/content/2011/1/26 |
| work_keys_str_mv | AT roemerflorian multidimensionalmodelorderselection AT haardtmartin multidimensionalmodelorderselection AT dacostajo227o multidimensionalmodelorderselection AT desousarafael multidimensionalmodelorderselection |