Sparse regularized low-rank tensor regression with applications in genomic data analysis

作者：

Highlights：

• A novel tensor regression model is introduced to simultaneously capture the underlying low-rank and sparse structure of the coefficient tensor.

• Unlike traditional tensor regression models that impose sparse penalty on the factor matrices of the coefficient tensor, our model directly imposes sparse penalty on the coefficient tensor.

• An orthonormality constraint is imposed on the factor matrices to make our model identifiable.

• We evaluate the proposed model on synthetic and real data sets. The results show that our model achieves competitive performance.

摘要

•A novel tensor regression model is introduced to simultaneously capture the underlying low-rank and sparse structure of the coefficient tensor.•Unlike traditional tensor regression models that impose sparse penalty on the factor matrices of the coefficient tensor, our model directly imposes sparse penalty on the coefficient tensor.•An orthonormality constraint is imposed on the factor matrices to make our model identifiable.•We evaluate the proposed model on synthetic and real data sets. The results show that our model achieves competitive performance.

论文关键词：Tensor regression,Tensor decomposition,Sparse penalty

论文评审过程：Received 13 March 2019, Revised 5 May 2020, Accepted 23 June 2020, Available online 1 July 2020, Version of Record 6 July 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107516