Convex Optimization
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Locally linear metric adaptation for semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Improving embeddings by flexible exploitation of side information
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Robust distance metric learning with auxiliary knowledge
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Guided Locally Linear Embedding
Pattern Recognition Letters
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Intelligent photo clustering with user interaction and distance metric learning
Pattern Recognition Letters
A supervised non-linear dimensionality reduction approach for manifold learning
Pattern Recognition
Relaxed pairwise learned metric for person re-identification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Parameterless Local Discriminant Embedding
Neural Processing Letters
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There has been much recent attention to the problem of learning an appropriate distance metric, using class labels or other side information. Some proposed algorithms are iterative and computationally expensive. In this paper, we show how to solve one of these methods with a closed-form solution, rather than using semidefinite programming. We provide a new problem setup in which the algorithm performs better or as well as some standard methods, but without the computational complexity. Furthermore, we show a strong relationship between these methods and the Fisher Discriminant Analysis.