Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Online and batch learning of pseudo-metrics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Metric Learning for Text Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Kernel-based distance metric learning for content-based image retrieval
Image and Vision Computing
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Metric learning by discriminant neighborhood embedding
Pattern Recognition
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
An efficient algorithm for local distance metric learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Neighborhood MinMax projections
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel-based metric learning for semi-supervised clustering
Neurocomputing
Generalized iterative RELIEF for supervised distance metric learning
Pattern Recognition
A Kernel Approach for Semisupervised Metric Learning
IEEE Transactions on Neural Networks
Gabor feature-based fast neighborhood component analysis for face recognition
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Face recognition using fast neighborhood component analysis with spatially smooth regularizer
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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Distance metric is of considerable importance in varieties of machine learning and pattern recognition applications. Neighborhood component analysis (NCA), one of the most successful metric learning algorithms, suffers from the high computational cost, which makes it only suitable for small-scale classification tasks. To overcome this disadvantage, we proposed a fast neighborhood component analysis (FNCA) method. For a given sample, FNCA adopts a local probability distribution model constructed based on its K nearest neighbors from the same class and from the different classes. We further extended FNCA to nonlinear metric learning scenarios using the kernel trick. Experimental results show that, compared with NCA, FNCA not only significantly increases the training speed but also obtains higher classification accuracy. Furthermore, comparative studies with the state-of-the-art approaches on various real-world datasets also verify the effectiveness of the proposed linear and nonlinear FNCA methods.