DeepAI AI Chat
Log In Sign Up

Bayesian Active Distance Metric Learning

by   Liu Yang, et al.

Distance metric learning is an important component for many tasks, such as statistical classification and content-based image retrieval. Existing approaches for learning distance metrics from pairwise constraints typically suffer from two major problems. First, most algorithms only offer point estimation of the distance metric and can therefore be unreliable when the number of training examples is small. Second, since these algorithms generally select their training examples at random, they can be inefficient if labeling effort is limited. This paper presents a Bayesian framework for distance metric learning that estimates a posterior distribution for the distance metric from labeled pairwise constraints. We describe an efficient algorithm based on the variational method for the proposed Bayesian approach. Furthermore, we apply the proposed Bayesian framework to active distance metric learning by selecting those unlabeled example pairs with the greatest uncertainty in relative distance. Experiments in classification demonstrate that the proposed framework achieves higher classification accuracy and identifies more informative training examples than the non-Bayesian approach and state-of-the-art distance metric learning algorithms.


page 1

page 2

page 3

page 4


Iterated Support Vector Machines for Distance Metric Learning

Distance metric learning aims to learn from the given training data a va...

Bayesian Neighbourhood Component Analysis

Learning a good distance metric in feature space potentially improves th...

Large-scale Distance Metric Learning with Uncertainty

Distance metric learning (DML) has been studied extensively in the past ...

Query-augmented Active Metric Learning

In this paper we propose an active metric learning method for clustering...

Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval

We propose the first Bayesian encoder for metric learning. Rather than r...

Secure Metric Learning via Differential Pairwise Privacy

Distance Metric Learning (DML) has drawn much attention over the last tw...

Similarity Function Tracking using Pairwise Comparisons

Recent work in distance metric learning has focused on learning transfor...