Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Classification from Labeled and Unlabeled Documents using EM
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Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Boosting margin based distance functions for clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Word sense disambiguation using label propagation based semi-supervised learning
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Information-theoretic metric learning
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Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Output Regularized Metric Learning with Side Information
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
An efficient algorithm for local distance metric learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
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IEEE Transactions on Multimedia - Special section on communities and media computing
Semi-Supervised Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Bidirectional PCA with assembled matrix distance metric for image recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized Manifold-Ranking-Based Image Retrieval
IEEE Transactions on Image Processing
K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis
Information Sciences: an International Journal
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Machine learning algorithms frequently suffer from the in sufficiency of training data and the usage of inappropriate distance metric. In this paper, we propose a joint learning of labels and distance metric (JLLDM) approach, which is able to simultaneously address the two difficulties. In comparison with the existing semi-supervised learning and distance metric learning methods that focus only on label prediction or distance metric construction, the JLLDM algorithm optimizes the labels of unlabeled samples and a Mahalanobis distance metric in a unified scheme. The advantage of JLLDM is multifold: 1) the problem of training data insufficiency can be tackled; 2) a good distance metric can be constructed with only very few training samples; and 3) no radius parameter is needed since the algorithm automatically determines the scale of the metric. Extensive experiments are conducted to compare the JLLDM approach with different semi-supervised learning and distance metric learning methods, and empirical results demonstrate its effectiveness.