L1-Norm-Based 2DPCA
| Data(s) |
01/08/2010
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|---|---|
| Resumo |
In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages. |
| Identificador | |
| Idioma(s) |
英语 |
| Palavras-Chave | #电子、电信技术::信号与模式识别 #电子、电信技术::计算机应用其他学科（含图像处理） #L1 norm #outlier #subspace #two-dimensional principal component analysis (2DPCA) |
| Tipo |
期刊论文 |
