Metric learning is fundamental to lots of learning algorithms and it plays significant roles in many applications. In this paper, we present a LogDet divergence based metric learning approach to a learn Mahalanobis distance over input space of the instances. In the proposed model, the most natural constraint triplets are used as the labels of the training samples. Meanwhile, in order to avoid o...

In order to rationally evaluate the high speed railway operation safety level, the environmental safety evaluation index system of high speed railway should be well established by means of analyzing the impact mechanism of severe weather such as raining, thundering, lightning, earthquake, winding, and snowing. In addition to that, the attribute recognition will be identified to determine the si...

Fuzzy C-Means (FCM) and hard clustering are the most common tools for data partitioning. However, the presence of noisy observations in the data may cause generation of completely unreliable partitions from these clustering algorithms. Also, application of the Euclidean distance in FCM only produces spherical clusters. In this paper, a new noise-rejection clustering algorithm based on Mahalanob...

Abstract In next generation wireless communications, the evolution of the mobile terminal towards a multimode architecture, will allow the mobile users to benefit simultaneously from various radio access technologies (RAT’s). The most important issue is how to choose the most appropriate time to start a redirection of traffic flow, and how to choose the most suitable network in terms of quality...

The FERET evaluation compared recognition rates for different semi-automated and automated face recognition algorithms. We extend FERET by considering when differences in recognition rates are statistically distinguishable subject to changes in test imagery. Nearest Neighbor classifiers using principal component and linear discriminant subspaces are compared using different choices of distance ...

Recently, Mahalanobis metric learning has gained a considerable interest for single-shot person re-identification. The main idea is to build on an existing image representation and to learn a metric that reflects the visual camera-to-camera transitions, allowing for a more powerful classification. The goal of this chapter is twofold. We first review the main ideas of Mahalanobis metric learning...