Neighborhood-Adaptive Structure Augmented Metric Learning

Discriminative Metric Learning by Neighborhood Gerrymandering

We formulate the problem of metric learning for k nearest neighbor classificationas a large margin structured prediction problem, with a latent variable representingthe choice of neighbors and the task loss directly corresponding to classificationerror. We describe an efficient algorithm for exact loss augmented inference, anda fast gradient descent algorithm for learning in thi...

Shrinkage Expansion Adaptive Metric Learning

Conventional pairwise constrained metric learning methods usually restrict the distance between samples of a similar pair to be lower than a fixed upper bound, and the distance between samples of a dissimilar pair higher than a fixed lower bound. Such fixed bound based constraints, however, may not work well when the intraand inter-class variations are complex. In this paper, we propose a shrin...

Multi-granularity distance metric learning via neighborhood granule margin maximization

Learning a distance metric from training samples is often a crucial step in machine learning and pattern recognition. Locality, compactness and consistency are considered as the key principles in distance metric learning. However, the existing metric learning methods just consider one or two of them. In this paper, we develop a multi-granularity distance learning technique. First, a new index, ...

Metric Learning with Adaptive Density Discrimination

Distance metric learning (DML) approaches learn a transformation to a representation space where distance is in correspondence with a predefined notion of similarity. While such models offer a number of compelling benefits, it has been difficult for these to compete with modern classification algorithms in performance and even in feature extraction. In this work, we propose a novel approach exp...

Learning the Structure of Augmented Bayesian Classifiers