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Tensor Response Regression with Low Tubal Rank and Sparsity

Abstract Number:

2164 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Jiping Wang (1), Xin Zhang (2)

Institutions:

(1) N/A, N/A, (2) Florida State University, N/A

Co-Author:

Xin Zhang  
Florida State University

First Author:

Jiping Wang  
N/A

Presenting Author:

Jiping Wang  
N/A

Abstract Text:

For contemporary scientific data, complicated structured tensor data with high dimensions are everywhere. Motivated by modeling the relationship between the multivariate covariates with the complicated tensor response, we proposed a tensor response model with low tubal rank and sparsity constraint. The low tubal rank constraint can capture the space-shifting or time-shifting characteristic of the data while sparsity can reduce the number of free parameters.

One special case of our model is equivalent to the multivariate reduced rank regression model. We also put forward a proven convergent ADMM algorithm that can obtain the optimized estimation efficiently. Simulations show that our method significantly outperforms the existing tensor response models.

Keywords:

ADMM|multidimensional array|multivariate linear regression|reduced rank regression|tubal rank|fourier transform

Sponsors:

Section on Statistical Learning and Data Science

Tracks:

Dimension Reduction

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I have read and understand that JSM participants must abide by the Participant Guidelines.

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I understand that JSM participants must register and pay the appropriate registration fee by June 3, 2025. The registration fee is non-refundable.

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