Local discriminative distance metrics ensemble learning

Publication Type:
Journal Article
Pattern Recognition, 2013, 46 (8), pp. 2337 - 2349
Issue Date:
Filename Description Size
Thumbnail2012004071OK.pdf1.17 MB
Adobe PDF
Full metadata record
The ultimate goal of distance metric learning is to incorporate abundant discriminative information to keep all data samples in the same class close and those from different classes separated. Local distance metric methods can preserve discriminative information by considering the neighborhood influence. In this paper, we propose a new local discriminative distance metrics (LDDM) algorithm to learn multiple distance metrics from each training sample (a focal sample) and in the vicinity of that focal sample (focal vicinity), to optimize local compactness and local separability. Those locally learned distance metrics are used to build local classifiers which are aligned in a probabilistic framework via ensemble learning. Theoretical analysis proves the convergence rate bound, the generalization bound of the local distance metrics and the final ensemble classifier. We extensively evaluate LDDM using synthetic datasets and large benchmark UCI datasets. © 2013 Elsevier Ltd.
Please use this identifier to cite or link to this item: