Bayesian generalized fused lasso modeling via NEG distribution
This is an incremental improvement for researchers in statistical modeling and machine learning, offering a more versatile sparse model.
The paper tackled the problem of improving the fused lasso method for sparse modeling by proposing a Bayesian approach using a normal-exponential-gamma prior, which resulted in superior performance compared to the ordinary fused lasso in simulations and real data analyses.
The fused lasso penalizes a loss function by the $L_1$ norm for both the regression coefficients and their successive differences to encourage sparsity of both. In this paper, we propose a Bayesian generalized fused lasso modeling based on a normal-exponential-gamma (NEG) prior distribution. The NEG prior is assumed into the difference of successive regression coefficients. The proposed method enables us to construct a more versatile sparse model than the ordinary fused lasso by using a flexible regularization term. We also propose a sparse fused algorithm to produce exact sparse solutions. Simulation studies and real data analyses show that the proposed method has superior performance to the ordinary fused lasso.