Learning Bregman distance functions for semi-supervised clustering
Learning distance functions with side information plays a key role in many data mining applications. Conventional distance metric learning approaches often assume that the target distance function is represented in some form of Mahalanobis distance. These approaches usually work well when data are i...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2012
|
| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/2282 https://ink.library.smu.edu.sg/context/sis_research/article/3282/viewcontent/Learning_Bregman_Distance_Functions_with_Applications_to_Semi_Supervised_Clustering.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Learning distance functions with side information plays a key role in many data mining applications. Conventional distance metric learning approaches often assume that the target distance function is represented in some form of Mahalanobis distance. These approaches usually work well when data are in low dimensionality, but often become computationally expensive or even infeasible when handling high-dimensional data. In this paper, we propose a novel scheme of learning nonlinear distance functions with side information. It aims to learn a Bregman distance function using a nonparametric approach that is similar to Support Vector Machines. We emphasize that the proposed scheme is more general than the conventional approach for distance metric learning, and is able to handle high-dimensional data efficiently. We verify the efficacy of the proposed distance learning method with extensive experiments on semi-supervised clustering. The comparison with state-of-the-art approaches for learning distance functions with side information reveals clear advantages of the proposed technique. |
|---|