This paper presents a novel discriminative learning method, called Manifold Discriminant Analysis (MDA), to solve the problem of image set classification. By modeling each image s...
In pattern recognition, feature vectors are occasionally subject to non-negative constraints. This characteristic can be expressed by a cone in feature vector space. In this paper...
In this paper, we develop a general classification framework called Kullback-Leibler Boosting, or KLBoosting. KLBoosting has following properties. First, classification is based o...
Locality preserving projections (LPP) is a typical graph-based dimensionality reduction (DR) method, and has been successfully applied in many practical problems such as face recog...
As the accuracy of conventional classifiers, based only on a static partitioning of feature space, appears to be approaching a limit, it may be useful to consider alternative appro...