We present a Bayesian approach for jointly learning distance metrics for a large collection of potentially related learning tasks. We assume there exists a relatively smaller set of basis distance metrics and the distance metric for each task is a sparse, positively weighted combination of these basis distance metrics. The set of basis distance metrics and the combination weights are learned fr...

Mahalanobis distance metric takes feature weights and correlation into account in the distance computation, which can improve the performance of many similarity/dissimilarity based methods, such as kNN. Most existing distance metric learning methods obtain metric based on the raw features and side information but neglect the reliability of them. Noises or disturbances on instances will make cha...

In our previous comprehensive survey [41], we have categorized the disparate issues in distance metric learning. Within each of the four categories, we have summarized existing work, disclosed their essential connections, strengths and weaknesses. The first category is supervised distance metric learning, which contains supervised global distance metric learning, local adaptive supervised dista...

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...

In this paper, we propose a feature weighting method that works in both the input space and the kernel-induced feature space. It assumes only the availability of similarity (dissimilarity) information, and the number of parameters in the transformation does not depend on the number of features. Besides feature weighting, it can also be regarded as performing nonparametric kernel adaptation. Exp...

Multi-modal data is dramatically increasing with the fast growth of social media. Learning a good distance measure for data with multiple modalities is of vital importance for many applications, including retrieval, clustering, classification and recommendation. In this paper, we propose an effective and scalable multi-modal distance metric learning framework. Based on the multi-wing harmonium ...

Distance metric learning has been widely investigated in machine learning and information retrieval. In this paper, we study a particular content-based image retrieval application of learning distance metrics from historical relevance feedback log data, which leads to a novel scenario called collaborative image retrieval. The log data provide the side information expressed as relevance judgemen...

In this paper, we examine the generalization error of regularized distance metric learning. We show that with appropriate constraints, the generalization error of regularized distance metric learning could be independent from the dimensionality, making it suitable for handling high dimensional data. In addition, we present an efficient online learning algorithm for regularized distance metric l...

Graphs are versatile tools for representing structured data. As a result, variety of machine learning methods have been studied graph data analysis. Although many such depend on the measurement differences between input graphs, defining an appropriate distance metric graphs remains controversial issue. Hence, we propose supervised method classification problem. Our method, named interpretable (...