Two-Dimensional Variable Selection and Its Applications in the Diagnostics of Product Quality Defects

TL;DR

This paper introduces a novel penalized matrix regression method for two-dimensional variable selection, aiding root-cause diagnostics in manufacturing by identifying key process variables and stages.

Contribution

It proposes a new penalized matrix regression model with a block coordinate proximal descent algorithm, ensuring convergence and effective variable selection in multistage manufacturing diagnostics.

Findings

The method accurately identifies crucial process variables and stages.

The BCPD algorithm converges to a critical point regardless of initialization.

Validation on real-world data demonstrates effectiveness.

Abstract

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 paper 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.