An alternative formulation of kernel LPP with application to image recognition

作者：Feng, Guiyu; Hu, Dewen^*; Zhang, David; Zhou, Zongtan

来源：Neurocomputing, 2006, 69(13-15): 1733-1738.

DOI：10.1016/j.neucom.2006.01.006

• kernel principal component analysis (KPCA)
• locality preserving projections (LPP)
• kernel LPP (KLPP)
• image recognition

Locality preserving projections (LPP) is a new subspace feature extraction method which seeks to preserve the local structure and intrinsic geometry of the data space. As the LPP model is linear, it may fail to extract the nonlinear features. This paper proposes to address this problem using an alternative formulation, kernel locality preserving projections (KLPP). Our algorithm consists of two steps: kernel principal component analysis (KPCA) plus LPP. We provide an outline for implementing KLPP. Experiments on the ORL face database and PolyU palmprint database demonstrate the effectiveness of the proposed algorithm.

• 出版日期2006-8
• 单位香港理工大学; 中国人民解放军国防科学技术大学