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Principal Component Analysis with Tensor Train Subspace
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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.
Forward citations
Cited by 2 Pith papers
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TTPUDR tensorizes the LPP mapping with tensor-trains and a Frobenius-norm objective, claiming robust and storage-efficient dimensionality reduction for high-dimensional tensor data.
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