title: Bilinear analysis for kernel selection and nonlinear feature extraction.
abstract: This paper presents a unified criterion, Fisher + kernel criterion (FKC), for feature extraction and recognition. This new criterion is intended to extract the most discriminant features in different nonlinear spaces, and then, fuse these features under a unified measurement. Thus, FKC can simultaneously achieve nonlinear discriminant analysis and kernel selection. In addition, we present an efficient algorithm Fisher + kernel analysis (FKA), which utilizes the bilinear analysis, to optimize the new criterion. This FKA algorithm can alleviate the ill-posed problem existed in traditional kernel discriminant analysis (KDA), and usually, has no singularity problem. The effectiveness of our proposed algorithm is validated by a series of face-recognition experiments on several different databases.
authors.affiliation: Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA. sophyshu@gmail.com
authors.forename: Shu
authors.initials: S
authors.lastname: Yang
authors.forename: Shuicheng
authors.initials: S
authors.lastname: Yan
authors.forename: Chao
authors.initials: C
authors.lastname: Zhang
authors.forename: Xiaoou
authors.initials: X
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity