Modern Information Retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Convex Optimization
Learning low-rank kernel matrices
ICML '06 Proceedings of the 23rd international conference on Machine learning
Improved error reporting for software that uses black-box components
Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Measuring the similarity between implicit semantic relations from the web
Proceedings of the 18th international conference on World wide web
Semi-supervised sparse metric learning using alternating linearization optimization
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Transfer metric learning by learning task relationships
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach
The Journal of Machine Learning Research
Learning discriminative projections for text similarity measures
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Metric learning for reinforcement learning agents
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Transfer Metric Learning with Semi-Supervised Extension
ACM Transactions on Intelligent Systems and Technology (TIST)
BoostML: an adaptive metric learning for nearest neighbor classification
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Metric and kernel learning using a linear transformation
The Journal of Machine Learning Research
Correlated attribute transfer with multi-task graph-guided fusion
Proceedings of the 20th ACM international conference on Multimedia
Optimal semi-supervised metric learning for image retrieval
Proceedings of the 20th ACM international conference on Multimedia
Spontaneous facial expression recognition: A robust metric learning approach
Pattern Recognition
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The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific notions of similarity for the problem at hand. The distance metric learning problem seeks to optimize a distance function subject to constraints that arise from fully-supervised or semisupervised information. Several recent algorithms have been proposed to learn such distance functions in low dimensional settings. One major shortcoming of these methods is their failure to scale to high dimensional problems that are becoming increasingly ubiquitous in modern data mining applications. In this paper, we present metric learning algorithms that scale linearly with dimensionality, permitting efficient optimization, storage, and evaluation of the learned metric. This is achieved through our main technical contribution which provides a framework based on the log-determinant matrix divergence which enables efficient optimization of structured, low-parameter Mahalanobis distances. Experimentally, we evaluate our methods across a variety of high dimensional domains, including text, statistical software analysis, and collaborative filtering, showing that our methods scale to data sets with tens of thousands or more features. We show that our learned metric can achieve excellent quality with respect to various criteria. For example, in the context of metric learning for nearest neighbor classification, we show that our methods achieve 24% higher accuracy over the baseline distance. Additionally, our methods yield very good precision while providing recall measures up to 20% higher than other baseline methods such as latent semantic analysis.