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arXiv · 1712.00828

Tensor Train Neighborhood Preserving Embedding

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.

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BibTeXRIS

Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron. 2018-03-10. Tensor Train Neighborhood Preserving Embedding. https://doi.org/10.1109/tsp.2018.2816568

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