Learning a Mixture of Sparse Distance Metrics for Classification and Dimensionality Reduction
Abstract
This paper extends the neighborhood components analysis method (NCA) to learning a mixture of sparse distance metrics for classification and dimensionality reduction. We emphasize two important properties in the recent learning literature, locality and sparsity, and (1) pursue a set of local distance metrics by maximizing a conditional likelihood of observed data; and (2) add l1-norm of eigenvalues of the distance metric to favor low rank matrices of fewer parameters. Experimental results on standard UCI machine learning datasets, face recognition datasets, and image categorization datasets demonstrate the feasibility of our approach for both distance metric learning and dimensionality reduction.
Cite
Text
Hong et al. "Learning a Mixture of Sparse Distance Metrics for Classification and Dimensionality Reduction." IEEE/CVF International Conference on Computer Vision, 2011. doi:10.1109/ICCV.2011.6126332Markdown
[Hong et al. "Learning a Mixture of Sparse Distance Metrics for Classification and Dimensionality Reduction." IEEE/CVF International Conference on Computer Vision, 2011.](https://mlanthology.org/iccv/2011/hong2011iccv-learning/) doi:10.1109/ICCV.2011.6126332BibTeX
@inproceedings{hong2011iccv-learning,
title = {{Learning a Mixture of Sparse Distance Metrics for Classification and Dimensionality Reduction}},
author = {Hong, Yi and Li, Quannan and Jiang, Jiayan and Tu, Zhuowen},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2011},
pages = {906-913},
doi = {10.1109/ICCV.2011.6126332},
url = {https://mlanthology.org/iccv/2011/hong2011iccv-learning/}
}