Grado Department of Industrial and Systems Engineering

Permanent URI for this community

https://hdl.handle.net/10919/24217

Browse

Browsing Grado Department of Industrial and Systems Engineering by Department "Statistics"

Now showing 1 - 2 of 2
  • Thumbnail Image
    The CCP Selector: Scalable Algorithms for Sparse Ridge Regression from Chance-Constrained Programming
    Xie, Weijun; Deng, Xinwei (2018-06-11)
    Sparse regression and variable selection for large-scale data have been rapidly developed in the past decades. This work focuses on sparse ridge regression, which considers the exact $L_0$ norm to pursue the sparsity. We pave out a theoretical foundation to understand why many existing approaches may not work well for this problem, in particular on large-scale datasets. Inspired by reformulating the problem as a chance-constrained program, we derive a novel mixed integer second order conic (MISOC) reformulation and prove that its continuous relaxation is equivalent to that of the convex integer formulation proposed in a recent work. Based upon these two formulations, we develop two new scalable algorithms, the greedy and randomized algorithms, for sparse ridge regression with desirable theoretical properties. The proposed algorithms are proved to yield near-optimal solutions under mild conditions. In the case of much larger dimensions, we propose to integrate the greedy algorithm with the randomized algorithm, which can greedily search the features from the nonzero subset identified by the continuous relaxation of the MISOC formulation. The merits of the proposed methods are elaborated through a set of numerical examples in comparison with several existing ones.
  • Thumbnail Image
    MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing
    Wang, Lening; Du, Pang; Jin, Ran (MDPI, 2021-01-01)
    Smart manufacturing, which integrates a multi-sensing system with physical manufacturing processes, has been widely adopted in the industry to support online and real-time decision making to improve manufacturing quality. A multi-sensing system for each specific manufacturing process can efficiently collect the in situ process variables from different sensor modalities to reflect the process variations in real-time. However, in practice, we usually do not have enough budget to equip too many sensors in each manufacturing process due to the cost consideration. Moreover, it is also important to better interpret the relationship between the sensing modalities and the quality variables based on the model. Therefore, it is necessary to model the quality-process relationship by selecting the most relevant sensor modalities with the specific quality measurement from the multi-modal sensing system in smart manufacturing. In this research, we adopted the concept of best subset variable selection and proposed a new model called Multi-mOdal beSt Subset modeling (MOSS). The proposed MOSS can effectively select the important sensor modalities and improve the modeling accuracy in quality-process modeling via functional norms that characterize the overall effects of individual modalities. The significance of sensor modalities can be used to determine the sensor placement strategy in smart manufacturing. Moreover, the selected modalities can better interpret the quality-process model by identifying the most correlated root cause of quality variations. The merits of the proposed model are illustrated by both simulations and a real case study in an additive manufacturing (i.e., fused deposition modeling) process.