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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jiwen Dong Xiuli Li |
| Copyright Year | 2012 |
| Description | Author affiliation: School of Information Science and Engineering, based Intelligent Computing, University of Jinan, Jinan, China (Jiwen Dong) || School of Information Science and Engineering, University of Jinan, Jinan, China (Xiuli Li) |
| Abstract | In order to reduce the original algorithm's dependence on the primary matrix and raise the classification accuracy, we combine the five order Newton method and the steepest descent method. We also introduce punishing factors into algorithm, and apply them to the core of the iterative process of the Fast ICA algorithm, an improved kernel independent component analysis algorithm (KICA) is proposed in this paper. The improved algorithm is applied to face feature extraction. The traditional KICA algorithm and the improved algorithm are respectively used to extract features. Through the experiment of the ORL face database. We prove that the improved KICA algorithm is good for the separation of the independent component, and its classification accuracy is improved. |
| Starting Page | 704 |
| Ending Page | 709 |
| File Size | 167039 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467302418 |
| e-ISBN | 9781467302425 |
| DOI | 10.1109/ICCSE.2012.6295172 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-07-14 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Databases Face recognition Independent component analysis Kernel independent component analysis Newton iteration Feature extraction Classification algorithms Face Steepest descent Kernel FastICA |
| Content Type | Text |
| Resource Type | Article |
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