This paper presents a method for learning a distance metric from relative comparison such as “A is closer to B than A is to C”. Taking a Support Vector Machine (SVM) approach, we develop an algorithm that provides a flexible way of describing qualitative training data as a set of constraints. We show that such constraints lead to a convex quadratic programming problem that can be solved by adap...

Recent work in metric learning has significantly improved the state-of-the-art in k-nearest neighbor classification. Support vector machines (SVM), particularly with RBF kernels, are amongst the most popular classification algorithms that uses distance metrics to compare examples. This paper provides an empirical analysis of the efficacy of three of the most popular Mahalanobis metric learning ...

Generalizing a property from a set of objects to a new object is a fundamental problem faced by the human cognitive system, and a long-standing topic of investigation in psychology. Classic analyses suggest that the probability with which people generalize a property from one stimulus to another depends on the distance between those stimuli in psychological space. This raises the question of ho...

Aspect phrase grouping is an important task in aspect-level sentiment analysis. It is a challenging problem due to polysemy and context dependency. We propose an Attention-based Deep Distance Metric Learning (ADDML) method, by considering aspect phrase representation as well as context representation. First, leveraging the characteristics of the review text, we automatically generate aspect phr...

Learning distance metrics is a fundamental problem in machine learning. Previous distance-metric learning research assumes that the training and test data are drawn from the same distribution, which may be violated in practical applications. When the distributions differ, a situation referred to as covariate shift, the metric learned from training data may not work well on the test data. In thi...

Medical image registration has received considerable attention in medical imaging and computer vision, because of the large variety of ways in which it can impact patient care. Over the years, many algorithms have been proposed for medical image registration. Medical image registration uses techniques to create images of parts of the human body for clinical purposes. This thesis focuses on one ...

Most of the existing metric learning methods are accomplished by exploiting pairwise constraints over the labeled data and frequently suffer from the insufficiency of training examples. To learn a robust distance metric from few labeled examples, prior knowledge from unlabeled examples as well as the metrics previously derived from auxiliary data sets can be useful. In this paper, we propose to...

Distance-based methods in pattern recognition and machine learning have to rely on a similarity or dissimilarity measure between patterns in the input space. For many applications, Euclidean distance in the input space is not a good choice and hence more complicated distance metrics have to be used. In this paper, we propose a parametric method for metric learning based on class label informati...

The performance of many computer vision and machine learning algorithms are heavily depend on the distance metric between samples. It is necessary to e xploit abundant of side information like pairwise constraints to learn a robust and reliable distance metric. While in real world application, large quantities of labeled data is unavailable due to the high labeling cost. Transfer distance metri...

This paper presents an algorithm which learns a distance metric from a data set by knowledge embedding and uses the new distance metric to solve nonlinear pattern recognition problems such a clustering. ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.