Corpus ID: 52965823

Learning Tensor Latent Features

@article{Chang2018LearningTL,
  title={Learning Tensor Latent Features},
  author={Sung-En Chang and Xun Zheng and Ian En-Hsu Yen and Pradeep Ravikumar and Rose Yu},
  journal={ArXiv},
  year={2018},
  volume={abs/1810.04754}
}
We study the problem of learning latent feature models (LFMs) for tensor data commonly observed in science and engineering such as hyperspectral imagery. However, the problem is challenging not only due to the non-convex formulation, the combinatorial nature of the constraints in LFMs, but also the high-order correlations in the data. In this work, we formulate a tensor latent feature learning problem by representing the data as a mixture of high-order latent features and binary codes, which… Expand

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