Discriminative Angle Feature Learning for Open-Set Deep Fault Classification

Exemplar based Deep Discriminative and Shareable Feature Learning for scene image classification

In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to hierarchically learn feature transformation filter banks to transform raw pixel image patches to features. The learned filter banks are expected to: (1) encode common visu...

A Discriminative Feature Learning Approach for Deep Face Recognition

Convolutional neural networks (CNNs) have been widely used in computer vision community, significantly improving the stateof-the-art. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new supervision signal, called center loss, for ...

Prototype Discriminative Learning for Face Image Set Classification

Discriminative Learning of the Prototype Set for Nearest Neighbor Classification

The nearest neighbor rule is one of the most widely used models for classification and selecting a compact set of prototype instances is an important problem for its applications. Many existing approaches on the prototype selection problem rely on instance-based analyses and local criteria on the class distribution, which are intractable for numerical optimization techniques. In this paper, we ...

Discriminative Feature Selection for Uncertain Graph Classification

Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objects are certain, and the binary edges within each graph represent the "presenc...