Image classification with manifold learning for out-of-sample data

@article{Han2013ImageCW,
  title={Image classification with manifold learning for out-of-sample data},
  author={Yahong Han and Zhongwen Xu and Zhigang Ma and Zi Xuan Huang},
  journal={Signal Processing},
  year={2013},
  volume={93},
  pages={2169-2177}
}
The successful applications of manifold learning in computer vision and multimedia research show that the geodesic distance along the manifold is more meaningful than Euclidean distance in the linear space. Therefore, in order to get better performance of image classification, it is preferable to have classifier defined on the low-dimensional manifold. However, most of the manifold learning algorithms do not provide explicit mapping of the unseen data. In this paper, we propose a framework of… CONTINUE READING
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