Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
SemiBoost: Boosting for Semi-Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised metric learning using pairwise constraints
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Entropy Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active reranking for web image search
IEEE Transactions on Image Processing
Semi-supervised distance metric learning for collaborative image retrieval and clustering
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Semi-supervised sparse metric learning using alternating linearization optimization
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-Supervised Learning
Distance metric learning by minimal distance maximization
Pattern Recognition
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse transfer learning for interactive video search reranking
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A Kernel Approach for Semisupervised Metric Learning
IEEE Transactions on Neural Networks
Linear dependency modeling for feature fusion
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Learning an appropriate distance metric is a critical problem in pattern recognition. This paper addresses the problem of semi-supervised metric learning. We propose a new regularized semi-supervised metric learning (RSSML) method using local topology and triplet constraints. Our regularizer is designed and developed based on local topology, which is represented by local neighbors from the local smoothness, cluster (low density) and manifold information point of view. The regularizer is then combined with the large margin hinge loss on the triplet constraints. In other words, we keep a large margin between different labeled samples, and in the meanwhile, we use the unlabeled samples to regularize it. Then the semi-supervised metric learning method is developed. We have performed experiments on classification using publicly available databases to evaluate the proposed method. To our best knowledge, this is the only method satisfying all the three semi-supervised assumptions, namely smoothness, cluster (low density) and manifold. Experimental results have shown that the proposed method outperforms state-of-the-art semi-supervised distance metric learning algorithms.