Learn More
0167-8655/$ see front matter 2009 Elsevier B.V. A doi:10.1016/j.patrec.2009.11.005 * Corresponding author. Tel.: +86 25 84896481x12 E-mail address: s.chen@nuaa.edu.cn (S. Chen). Single training image face recognition is one of the main challenges to appearance-based pattern recognition techniques. Many classical dimensionality reduction methods such as LDA(More)
Graph-based dimensionality reduction (DR) methods play an increasingly important role in many machine learning and pattern recognition applications. In this paper, we propose a novel graph-based learning scheme to conduct Graph Optimization for Dimensionality Reduction with Sparsity Constraints (GODRSC). Different from most of graph-based DR methods where(More)
Functional brain network analysis has become one principled way of revealing informative organization architectures in healthy brains, and providing sensitive biomarkers for diagnosis of neurological disorders. Prior to any post hoc analysis, however, a natural issue is how to construct "ideal" brain networks given, for example, a set of functional magnetic(More)
The age sequence of human beings exhibits two striking characteristics: ordinal in age values and similar in facial appearance of neighboring ages. Although it has been demonstrated that such ordinality especially the neighboring similarity has positive influence on age estimation, existing approaches have yet not simultaneously taken the two types of(More)
Unlike traditional classification tasks, multilabel classification allows a sample to associate with more than one label. This generalization naturally arises the difficulty in classification. Similar to the single label classification task, neighborhood-based algorithms relying on the nearest neighbor have attracted lots of attention and some of them show(More)
In machine learning and computer vision fields, a wide range of applications, such as human age estimation and head pose recognition, are related to ordinal data in which there exists an order relationship. To perform such ordinal estimations in a desired metric space, in this work we first propose a novel ordinal margin metric learning (ORMML) method by(More)
We introduce a kernel learning algorithm, called kernel propagation (KP), to learn a nonparametric kernel from a mixture of a few pairwise constraints and plentiful unlabeled samples. Specifically, KP consists of two stages: the first is to learn a small-sized sub-kernel matrix just restricted to the samples with constrains, and the second is to propagate(More)