A Method for Detecting Globally Distributed Defects by Using Learning with Mahalanobis Distance

Semi-supervised Distributed Clustering with Mahalanobis Distance Metric Learning

Semi-supervised clustering uses a small amount of supervised information to aid unsupervised learning. As one of the semi-supervised clustering methods, metric learning has been widely used to clustering the centralized data points. However, there are many distributed data points, which cannot be centralized for the various reasons. Based on MPCK-MEANS framework [1] , the method of distributed ...

A Method for Defuzzication by Weighted Distance

Supervised learning using mahalanobis distance for record linkage

In data privacy, record linkage is a well known technique used to evaluate the disclosure risk of protected data. Mainly, the idea is the linkage between records of different databases, which make reference to the same individuals. In this paper we introduce a new parametrized variation of record linkage relying on the Mahalanobis distance, and a supervised learning method to determine the opti...

Supervised learning using mahalanobis distance for record linkage

In data privacy, record linkage is a well known technique used to evaluate the disclosure risk of protected data. Mainly, the idea is the linkage between records of different databases, which make reference to the same individuals. In this paper we introduce a new parametrized variation of record linkage relying on the Mahalanobis distance, and a supervised learning method to determine the opti...

Mahalanobis Distance Learning for Person Re-identification

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...