Duy Nhat Phan, Hoai An Le Thi, A Novel Approach for Estimating Multiple Sparse Precision Matrices Using ℓ0,0 Regularization

Abstract: We present a novel approach for estimating multiple sparse precision matrices using the ℓ0,0 regularization. The multiple precision matrices share some common structures such as the weights or locations of non-zero elements, we are interested in selecting simultaneously elements on each precision matrix as well as across multiple precision matrices. This is referred as bi-level variable selection. The optimization problem can be formulated as DC (Difference of Convex functions) programs to which DC programming and DCA (DC Algorithm) are investigated. The experimental results on both simulated and real datasets demonstrate the efficiency of our algorithms.

 

Keywords: Sparse matrices, Bi-level variable selection, DC programming, DCA, Multiple precision matrices.

 

Citation: Phan D.N. and Le Thi H.A., A Novel Approach for Estimating Multiple Sparse Precision Matrices Using ℓ0,0 Regularization, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Tokyo, Japan, pp. 726-733, 2017.

 

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