Sample Label-based PLS and Feature eztraction

摘要：

Partial least squares (PLS) method is an effective approach for regression analysis and image feature extraction. Non-iterative PLS based on orthogonal constraints can extract PLS features rapidly and effectively, while the features maybe correlative. PLS based on Uncorrelated Score Constraints can extract Uncorrelated features which make image recognition more effective. 2DPLS can extract features from image matrices directly, which can solve the small sample problems. Considering that the traditional class label encodings dont emphasize the significance of the samples in overlapping regions between classes, fuzzy k-NN method is employed in class label encodings to make use of the sample distributions, then improved algorithms of PLS and 2DPLS based on sample label encodings are given. The results of experiments on ORL face database show that the improved algorithms presented are better than traditional PLS, which can extract discriminative features more efficiently and robustly.

关键词： uncorrelated scores partial least squares (PLS) class label encoding feature eztraction face recognition

作者: Yang MaoLong Mao WeiHao Sun QuanSen Xia DeShen

作者单位: Nanjing University of Science & Technology, Nanjing 210094, China Nanjing University of Internationa Nanjing University of International Studies, Nanjing 210031, China Nanjing University of Science & Technology, Nanjing 210094, China

会议类型: 国际会议

会议名称: The 6th International Conference on Partial Least Squares and Related Methods(第六届偏最小二乘及相关方法国际会议)

会议地点: 北京

会议语种:英文

页码: 127-131

在线出版日期: 2009-09-04（万方平台首次上网日期，不代表论文的发表时间）