Convex Optimization
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Supervised dimensionality reduction via sequential semidefinite programming
Pattern Recognition
Multiple-View Geometry Under the {$L_\infty$}-Norm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trace quotient problems revisited
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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Due to its importance to classification and clustering, dimensionality reduction or distance metric learning has been studied in depth in recent years In this work, we demonstrate the weakness of a widely-used class separability criterion—trace quotient for dimensionality reduction—and propose new criteria for the dimensionality reduction problem The proposed optimization problem can be efficiently solved using semidefinite programming, similar to the technique in [1] Experiments on classification and clustering are performed to evaluate the proposed algorithm Results show the advantage of the our proposed algorithm.