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.


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Wang, Hua, Chris Ding, and Heng Huang. "Multi-label linear discriminant analysis." European Conference on Computer Vision. Springer, Berlin, Heidelberg, 2010.
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  title={Multi-label linear discriminant analysis},
  author={Wang, Hua and Ding, Chris and Huang, Heng},
  booktitle={European Conference on Computer Vision},