Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Learning Distance Metrics with Contextual Constraints for Image Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Learning distance function by coding similarity
Proceedings of the 24th international conference on Machine learning
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Distance metric learning vs. Fisher discriminant analysis
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Text categorization with knowledge transfer from heterogeneous data sources
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
An experts algorithm for transfer learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A scalable kernel-based algorithm for semi-supervised metric learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Transfer metric learning by learning task relationships
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Adapted transfer of distance measures for quantitative structure-activity relationships
DS'10 Proceedings of the 13th international conference on Discovery science
Transfer Metric Learning with Semi-Supervised Extension
ACM Transactions on Intelligent Systems and Technology (TIST)
Learning a distance metric by empirical loss minimization
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Neighborhood-Based smoothing of external cluster validity measures
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Extracting elite pairwise constraints for clustering
Neurocomputing
Learning image-to-class distance metric for image classification
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
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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 leverage such auxiliary knowledge to assist distance metric learning, which is formulated following the regularized loss minimization principle. Two algorithms are derived on the basis of manifold regularization and log-determinant divergence regularization technique, respectively, which can simultaneously exploit label information (i.e., the pairwise constraints over labeled data), unlabeled examples, and the metrics derived from auxiliary data sets. The proposed methods directly manipulate the auxiliary metrics and require no raw examples from the auxiliary data sets, which make them efficient and flexible. We conduct extensive evaluations to compare our approaches with a number of competing approaches on face recognition task. The experimental results show that our approaches can derive reliable distance metrics from limited training examples and thus are superior in terms of accuracy and labeling efforts.