Sparse regularized low-rank tensor regression with applications in genomic data analysis. (November 2020)

• Record Type: Journal Article
• Authors: Ou-Yang, Le; Zhang, Xiao-Fei; Yan, Hong
• Is Part Of: Pattern recognition. Volume 107(2020:Nov.)
• Publication Date: 2020-11
• Subjects: Tensor regression -- Tensor decomposition -- Sparse penalty; 00-01 -- 99-00; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107516
• Languages: English
• ISSNs: 0031-3203

Abstract

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. Abstract: Many applications in biomedical informatics deal with data in the tensor form. Traditional regression methods which take vectors as covariates may encounter difficulties in handling tensors due to their ultrahigh dimensionality and complex structure. In this paper, we introduce a novel sparse regularized Tucker tensor regression model to exploit the structure of tensor covariates and perform feature selection on tensor data. Based on Tucker decomposition of the regression coefficient tensor, we reduce the ultrahigh dimensionality to a manageable level. To make our model identifiable, we impose the orthonormality constraint on the factor matrices. Unlike previous 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 to select the relevant features on tensor data. An efficient optimization algorithm based on alternating direction method of multiplier (ADMM) algorithm is designed to solve our proposed model. The performance of our model is evaluated on both synthetic and real genomic data. Experiment results on synthetic data demonstrate that our model could identify the true related signals more accurately than other state-of-the-art regression models. The analysis on genomic data of melanoma demonstrates that our model can achieve better prediction performance and identify markers with important implications. Our model and the associated studies can provide useful insights to disease or pathogenesis mechanisms, and will benefit further studies in variable selection.