Multi-Label Linear Discriminant Analysis
Hua Wang, Chris Ding, Heng Huang
ECCV - 2010
Multi-label problems arise frequently in image and video annotations, and many other related applications such as multi-topic text categorization, music classification, etc. Like other computer vision tasks, multi-label image and video annotations also suffer from the difficulty of high dimensionality because images often have a large number of features. Linear discriminant analysis (LDA) is a well-known method for dimensionality reduction. However, the classical Linear Discriminant Analysis (LDA) only works for single-label multi-class classifications and cannot be directly applied to multi-label multi-class classifications. It is desirable to naturally generalize the classical LDA to multi-label formulations. At the same time, multi-label data present a new opportunity to improve classification accuracy through label correlations, which are absent in single-label data. In this work, we propose a novel Multi-label Linear Discriminant Analysis (MLDA) method to take advantage of label correlations and explore the powerful classification capability of the classical LDA to deal with multi-label multi-class problems. Extensive experimental evaluations on five public multi-label data sets demonstrate excellent performance of our method.
Links
- View publications from Hua Wang
- View publications presented in ECCV
- View publications researching Multiple-Label Learning
Cite this paper
MLA
Wang, Hua, Chris Ding, and Heng Huang. "Multi-label linear discriminant analysis." European Conference on Computer Vision. Springer, Berlin, Heidelberg, 2010.
BibTeX
@inproceedings{wang2010multi,
title={Multi-label linear discriminant analysis},
author={Wang, Hua and Ding, Chris and Huang, Heng},
booktitle={European Conference on Computer Vision},
pages={126--139},
year={2010},
organization={Springer}
}