Though AdaBoost has been widely used for feature selection and classifier learning, many of the selected features, or weak classifiers, are redundant. By incorporating mutual infor...
LinLin Shen, Li Bai, Daniel Bardsley, Yangsheng Wa...
Abstract. Principal Component Analysis (PCA) plus Linear Discriminant Analysis (LDA) (PCA+LDA) and LDA/QR are both two-stage methods that deal with the small sample size (SSS) prob...
— A novel face recognition method is proposed, in which face images are represented by a set of local labeled graphs, each containing information about the appearance and geometr...
In our camera based user interface, user inputs Chinese characters by moving fingertip with his/her fingers. Therefore, we call those characters “finger– written Chinese chara...
kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for...