Neural Network for Principal Component Analysis with Applications in Image Compression
Classical feature extraction and data projection methods have been extensively investigated in the pattern recognition and exploratory data analysis literature. Feature extraction and multivariate data projection allow avoiding the "curse of dimensionality", improve the generalization abil...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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International Institute of Informatics and Cybernetics
2007-04-01
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| Series: | Journal of Systemics, Cybernetics and Informatics |
| Subjects: | |
| Online Access: | http://www.iiisci.org/Journal/CV$/sci/pdfs/P291440.pdf
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| _version_ | 1819210932921827328 |
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| author | Luminita State Catalina Lucia Cocianu Vlamos Panayiotis |
| author_facet | Luminita State Catalina Lucia Cocianu Vlamos Panayiotis |
| author_sort | Luminita State |
| collection | DOAJ |
| description | Classical feature extraction and data projection methods have been extensively investigated in the pattern recognition and exploratory data analysis literature. Feature extraction and multivariate data projection allow avoiding the "curse of dimensionality", improve the generalization ability of classifiers and significantly reduce the computational requirements of pattern classifiers. During the past decade a large number of artificial neural networks and learning algorithms have been proposed for solving feature extraction problems, most of them being adaptive in nature and well-suited for many real environments where adaptive approach is required. Principal Component Analysis, also called Karhunen-Loeve transform is a well-known statistical method for feature extraction, data compression and multivariate data projection and so far it has been broadly used in a large series of signal and image processing, pattern recognition and data analysis applications. |
| first_indexed | 2024-12-23T06:19:02Z |
| format | Article |
| id | doaj.art-2e33dde5c8384ff98e01a1eb66270885 |
| institution | Directory Open Access Journal |
| issn | 1690-4524 |
| language | English |
| last_indexed | 2024-12-23T06:19:02Z |
| publishDate | 2007-04-01 |
| publisher | International Institute of Informatics and Cybernetics |
| record_format | Article |
| series | Journal of Systemics, Cybernetics and Informatics |
| spelling | doaj.art-2e33dde5c8384ff98e01a1eb662708852022-12-21T17:57:14ZengInternational Institute of Informatics and CyberneticsJournal of Systemics, Cybernetics and Informatics1690-45242007-04-01526265Neural Network for Principal Component Analysis with Applications in Image CompressionLuminita State0Catalina Lucia Cocianu1Vlamos Panayiotis2 Dept. of Computer Science, University of Pitesti Dept. of Computer Science, Academy of Economic Studies, Bucharest Hellenic Open University Classical feature extraction and data projection methods have been extensively investigated in the pattern recognition and exploratory data analysis literature. Feature extraction and multivariate data projection allow avoiding the "curse of dimensionality", improve the generalization ability of classifiers and significantly reduce the computational requirements of pattern classifiers. During the past decade a large number of artificial neural networks and learning algorithms have been proposed for solving feature extraction problems, most of them being adaptive in nature and well-suited for many real environments where adaptive approach is required. Principal Component Analysis, also called Karhunen-Loeve transform is a well-known statistical method for feature extraction, data compression and multivariate data projection and so far it has been broadly used in a large series of signal and image processing, pattern recognition and data analysis applications.http://www.iiisci.org/Journal/CV$/sci/pdfs/P291440.pdf Image Processingpattern recognitiondata compression/decompressionKarhunen-Loeve transformPCARLS algorithmfeature extraction |
| spellingShingle | Luminita State Catalina Lucia Cocianu Vlamos Panayiotis Neural Network for Principal Component Analysis with Applications in Image Compression Journal of Systemics, Cybernetics and Informatics Image Processing pattern recognition data compression/decompression Karhunen-Loeve transform PCA RLS algorithm feature extraction |
| title | Neural Network for Principal Component Analysis with Applications in Image Compression |
| title_full | Neural Network for Principal Component Analysis with Applications in Image Compression |
| title_fullStr | Neural Network for Principal Component Analysis with Applications in Image Compression |
| title_full_unstemmed | Neural Network for Principal Component Analysis with Applications in Image Compression |
| title_short | Neural Network for Principal Component Analysis with Applications in Image Compression |
| title_sort | neural network for principal component analysis with applications in image compression |
| topic | Image Processing pattern recognition data compression/decompression Karhunen-Loeve transform PCA RLS algorithm feature extraction |
| url | http://www.iiisci.org/Journal/CV$/sci/pdfs/P291440.pdf
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| work_keys_str_mv | AT luminitastate neuralnetworkforprincipalcomponentanalysiswithapplicationsinimagecompression AT catalinaluciacocianu neuralnetworkforprincipalcomponentanalysiswithapplicationsinimagecompression AT vlamospanayiotis neuralnetworkforprincipalcomponentanalysiswithapplicationsinimagecompression |