Scalable Large-Margin Mahalanobis Distance Metric Learning

Details Scalable Large-Margin Mahalanobis Distance Metric Learning Scalable Large-Margin Mahalanobis Distance Metric Learning

2010-03-02

arxiv.org/abs/1003.0487v1

Computer Science

Computer Vision and Pattern Recognition

To publish/Published in IEEE Transactions on Neural Networks, 2010

Scientific paper

For many machine learning algorithms such as $k$-Nearest Neighbor ($k$-NN) classifiers and $ k $-means clustering, often their success heavily depends on the metric used to calculate distances between different data points. An effective solution for defining such a metric is to learn it from a set of labeled training samples. In this work, we propose a fast and scalable algorithm to learn a Mahalanobis distance metric. By employing the principle of margin maximization to achieve better generalization performances, this algorithm formulates the metric learning as a convex optimization problem and a positive semidefinite (psd) matrix is the unknown variable. a specialized gradient descent method is proposed. our algorithm is much more efficient and has a better performance in scalability compared with existing methods. Experiments on benchmark data sets suggest that, compared with state-of-the-art metric learning algorithms, our algorithm can achieve a comparable classification accuracy with reduced computational complexity.

Affiliated with

Also associated with

No associations

Rating

Scalable Large-Margin Mahalanobis Distance Metric Learning does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Scalable Large-Margin Mahalanobis Distance Metric Learning, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Scalable Large-Margin Mahalanobis Distance Metric Learning will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-662928