Two-dimensional variable selection and its applications in the diagnostics of product quality defects

The root cause diagnostics of product quality defects in multistage manufacturing processes often requires a joint identification of crucial stages and process variables. To meet this requirement, this article proposes a novel penalized matrix regression methodology for two-dimensional variable selection. The method regresses a scalar response variable against a matrix-based predictor using a generalized linear model. The unknown regression coefficient matrix is decomposed as a product of two factor matrices. The rows of the first factor matrix and the columns of the second factor matrix are simultaneously penalized to inspire sparsity. To estimate the parameters, we develop a Block Coordinate Proximal Descent (BCPD) optimization algorithm, which cyclically solves two convex sub-optimization problems. We have proved that the BCPD algorithm always converges to a critical point with any initialization. In addition, we have also proved that each of the sub-optimization problems has a closed-form solution if the response variable follows a distribution whose (negative) log-likelihood function has a Lipschitz continuous gradient. A simulation study and a dataset from a real-world application are used to validate the effectiveness of the proposed method.

Automated summary

The proposed method introduces a novel penalized matrix regression methodology to diagnose the root cause of product quality defects in multistage manufacturing processes. By decomposing the unknown regression coefficient matrix into two factor matrices and penalizing their rows and columns simultaneously, sparsity is induced effectively. The Block Coordinate Proximal Descent (BCPD) optimization algorithm is developed for parameter estimation and solving convex sub-optimization problems cyclically.