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Learning Mahalanobis Metric For Large Margin KNN.pdf

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Preview of Learning Mahalanobis Metric for Large Margin kNN

🔗 Source: papers.nips.cc

📊 Size: 290 KB

👤 Author: Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul

⬇️ Downloads: 325

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presents a method to learn a Mahalanobis distance metric for k-nearest neighbor (kNN) classification using semidefinite programming. The goal is to make the k-nearest neighbors always belong to the same class while separating examples from different classes by a large margin. The authors show that this approach leads to significant improvements in kNN classification on seven datasets, achieving a test error rate of 1.3% on the MNIST handwritten digits dataset. The learning problem reduces to a convex optimization based on the hinge loss, similar to support vector machines (SVMs), but unlike SVMs, the framework requires no modification for multiway classification. The model learns a linear transformation to compute squared distances, with a cost function that penalizes large distances between inputs and their target neighbors, and small distances between inputs and differently labeled examples. The optimization is inspired by neighborhood component analysis and metric learning by energy-based models, but it is cast as an instance of semidefinite programming, making it convex and efficiently computable. The authors describe their approach as large margin nearest neighbor (LMNN) classification, viewing it as the counterpart to SVMs where kNN classification replaces linear classification.

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Learns Mahalanobis distance metric for k-NN classification, ensuring same-class neighbors and large inter-class margins. Improves kNN accuracy, e.g., 1.3% test error on MNIST.

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File Format: PDF
File Size: 290 KB
Pages: 8
Language: EN
Author: Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul
Total Downloads: 325
Last Updated: 3 weeks ago

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