Robust Sparse 2D Principal Component Analysis for Object Recognition
| Autor: | Xiaolong Zheng, Jicheng Meng |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Numerical Analysis
Computer science business.industry Applied Mathematics Sparse PCA Cognitive neuroscience of visual object recognition Pattern recognition Sparse approximation computer.software_genre Facial recognition system Class (biology) Computer Science Applications Computational Theory and Mathematics Face (geometry) Principal component analysis Outlier Data mining Artificial intelligence business computer Analysis |
| Zdroj: | Applied Mathematics & Information Sciences. 7:2509-2514 |
| ISSN: | 2325-0399 1935-0090 |
| DOI: | 10.12785/amis/070645 |
| Popis: | We extensively investigate robust sparse two dimensional principal component analysis (RS2DPCA) that makes the best of semantic, structural information and suppresses outliers in this paper . The RS2DPCA combines the advantages of sparsity, 2D data format and L1-norm for data analysis. We also prove that RS2DPCA can offer a good solution of seeking spare 2D principal components. To verify the performance of RS2DPCA in object recognition, experiments are performed on three famous face databases, i.e. Yale, ORL, and FERET, and the experimental results show that the proposed RS2DPCA outperform the same class of algorithms for face recognition, such as robust sparse PCA, L1-norm-based 2DPCA. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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