Dimensionality reduction via locally reconstructive patch alignment

作者：Chen, Yi^*; Yin, Jun; Zhu, Jie; Jin, Zhong

来源：Optical Engineering, 2012, 51(7): 077208.

DOI：10.1117/1.OE.51.7.077208

• dimensionality reduction
• patch alignment
• discriminative locality alignment
• face recognition
• finger knuckle-print recognition

Based on the local patch concept, we proposed locally reconstructive patch alignment (LRPA) for dimensionality reduction. For each patch, LRPA aims to find the low-dimensional subspace in which the reconstruction error of the within-class nearest neighbors is minimized and the reconstruction error of the between-class nearest neighbors is maximized. LRPA preserves the local structure hidden in the high-dimensional space. More importantly, LRPA has natural connections with linear regression classification (LRC). While LRC uses reconstruction errors as the classification rule, a sample can be classified correctly when the within-class reconstruction error is minimal. The goal of LRPA makes it cooperate well with LRC. The experimental results on the extended Yale B (YALE-B), AR, PolyU finger knuckle print, and the palm print databases demonstrate LRPA plus LRC is an effective and robust pattern-recognition system.

• 出版日期2012-7
• 单位南京理工大学; 上海海事大学; 南京晓庄学院