Discriminatively Embedded K-Means for Multi-view Clustering

@article{Xu2016DiscriminativelyEK,
  title={Discriminatively Embedded K-Means for Multi-view Clustering},
  author={Jinglin Xu and Junwei Han and Feiping Nie},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={5356-5364}
}
In real world applications, more and more data, for example, image/video data, are high dimensional and repre-sented by multiple views which describe different perspectives of the data. Efficiently clustering such data is a challenge. To address this problem, this paper proposes a novel multi-view clustering method called Discriminatively Embedded K-Means (DEKM), which embeds the synchronous learning of multiple discriminative subspaces into multi-view K-Means clustering to construct a unified… CONTINUE READING
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