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2013 | OriginalPaper | Chapter

Dimension Reduction for Tensor Classification

Authors : Peng Zeng, Wenxuan Zhong

Published in: Topics in Applied Statistics

Publisher: Springer New York

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Abstract

This article develops a sufficient dimension reduction method for high dimensional regression with tensor predictors, which extends the conventional vector-based dimension reduction model. It proposes a tensor dimension reduction model that assumes that a response depends on some low-dimensional representation of tensor predictors through an unspecified link function. A sequential iterative dimension reduction algorithm (SIDRA) that effectively utilizes the tensor structure is proposed to estimate the parameters. The SIDRA generalizes the method in Zhong and Suslick (2012), which proposes an iterative estimation algorithm for matrix classification. Preliminary studies demonstrate that the tensor dimension reduction model is a rich and flexible framework for high dimensional tensor regression, and SIDRA is a powerful and computationally efficient method.

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Metadata
Title
Dimension Reduction for Tensor Classification
Authors
Peng Zeng
Wenxuan Zhong
Copyright Year
2013
Publisher
Springer New York
Book
Topics in Applied Statistics

Print ISBN: 978-1-4614-7845-4

Electronic ISBN: 978-1-4614-7846-1

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-1-4614-7846-1_18

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