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Computer Science > Machine Learning

arXiv:1803.05026 (cs)
[Submitted on 13 Mar 2018]

Title:Principal Component Analysis with Tensor Train Subspace

Authors:Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron
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Abstract:Tensor train is a hierarchical tensor network structure that helps alleviate the curse of dimensionality by parameterizing large-scale multidimensional data via a set of network of low-rank tensors. Associated with such a construction is a notion of Tensor Train subspace and in this paper we propose a TT-PCA algorithm for estimating this structured subspace from the given data. By maintaining low rank tensor structure, TT-PCA is more robust to noise comparing with PCA or Tucker-PCA. This is borne out numerically by testing the proposed approach on the Extended YaleFace Dataset B.
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Information Theory (cs.IT); Numerical Analysis (math.NA)
Cite as: arXiv:1803.05026 [cs.LG]
  (or arXiv:1803.05026v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.1803.05026

Submission history

From: Vaneet Aggarwal [view email]
[v1] Tue, 13 Mar 2018 19:58:46 UTC (3,691 KB)
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