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Activity Number: 205 - New Direction for Model Selection in Big Data
Type: Invited
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract #322462
Title: Optimal Variable Selection via Sparse Tensor Decomposition
Author(s): Dong Wang*
Companies: Princeton University
Keywords: Sparse tensor decomposition ; Variable selection ; Principal component analysis
Abstract:

Traditional tensor decompositions have been widely used for dimension reduction of tensorial data. However, in the high dimensional setting, they have the same low interpretability as the traditional principal component analysis. In this talk, we propose an iterative thresholding algorithm for sparse tensor decomposition, which generalizes the higher order orthogonal iteration algorithm for traditional tensor decomposition. The minimax optimality of the proposed algorithm is studied under the spike model. We also demonstrate the numerical performance of the approach through simulations.


Authors who are presenting talks have a * after their name.

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