Tensor Train Neighborhood Preserving Embedding

Wenqi Wang; Vaneet Aggarwal; Shuchin Aeron

doi:10.1109/TSP.2018.2816568

Tensor Train Neighborhood Preserving Embedding

Machine Learning 2018-05-09 v2 Information Theory math.IT Machine Learning

Authors: Wenqi Wang , Vaneet Aggarwal , Shuchin Aeron

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Abstract

In this paper, we propose a Tensor Train Neighborhood Preserving Embedding (TTNPE) to embed multi-dimensional tensor data into low dimensional tensor subspace. Novel approaches to solve the optimization problem in TTNPE are proposed. For this embedding, we evaluate novel trade-off gain among classification, computation, and dimensionality reduction (storage) for supervised learning. It is shown that compared to the state-of-the-arts tensor embedding methods, TTNPE achieves superior trade-off in classification, computation, and dimensionality reduction in MNIST handwritten digits and Weizmann face datasets.

Keywords

tensor decomposition dimensionality reduction neural operator

Cite

@article{arxiv.1712.00828,
  title  = {Tensor Train Neighborhood Preserving Embedding},
  author = {Wenqi Wang and Vaneet Aggarwal and Shuchin Aeron},
  journal= {arXiv preprint arXiv:1712.00828},
  year   = {2018}
}

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Accepted to IEEE Transactions on Signal Processing, Mar 2018

Tensor Train Neighborhood Preserving Embedding

Abstract

Keywords

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