2010/03/02 by Chunhua Shen, Shen, Chunhua, Junae Kim +3
Computer Science · Engineering · #Face and Expression Recognition #Advanced Image and Video Retrieval Techniques #Remote-Sensing Image Classification
paper · pdf · doi:10.48550/arxiv.1003.0487
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.