Collaborative representation analysis methods for feature extraction
| Autor: | Mingu Ren, Juliang Hua, Huan Wang, Heyan Huang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry Feature extraction Pattern recognition 02 engineering and technology Sparse approximation Machine learning computer.software_genre 01 natural sciences Facial recognition system 010104 statistics & probability Kernel (linear algebra) Artificial Intelligence Kernel (statistics) Face (geometry) Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Artificial intelligence 0101 mathematics Representation (mathematics) business computer Software |
| Zdroj: | Neural Computing and Applications. 28:225-231 |
| ISSN: | 1433-3058 0941-0643 |
| DOI: | 10.1007/s00521-016-2299-3 |
| Popis: | Recently, sparse representation (SR) theory gets much success in the fields of pattern recognition and machine learning. Many researchers use SR to design classification methods and dictionary learning via reconstruction residual. It was shown that collaborative representation (CR) is the key part in sparse representation-based classification (SRC) and collaborative representation-based classification (CRC). Both SRC and CRC are good classification methods. Here, we give a collaborative representation analysis (CRA) method for feature extraction. Not like SRC-/CRC-based methods (e.g., SPP and CRP), CRA could directly extract the features like PCA and LDA. Further, a Kernel CRA (KCRA) is developed via kernel tricks. The experimental results on FERET and AR face databases show that CRA and KCRA are two effective feature extraction methods and could get good performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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