# Structured Point Cloud Data Analysis via Regularized Tensor Regression for Process Modeling and Optimization

arXiv: https://arxiv.org/abs/1807.10278 · HTML: https://mlsanity.com/paper/1807.10278/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2018.

## Built on by (most cited first, 8 of 8)

- [Deep Multistage Multi-Task Learning for Quality Prediction of Multistage Manufacturing Systems](https://mlsanity.com/paper/2105.08180/) (2021)
- [Federated Multilinear Principal Component Analysis with Applications in Prognostics](https://mlsanity.com/paper/2312.06050/) (2023)
- [Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data](https://mlsanity.com/paper/2309.03439/) (2023)
- [Tensor-on-tensor Regression Neural Networks for Process Modeling with High-dimensional Data](https://mlsanity.com/paper/2510.05329/) (2025)
- [ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data](https://mlsanity.com/paper/2202.13788/) (2022)
- [Maximum Covariance Unfolding Regression: A Novel Covariate-based Manifold Learning Approach for Point Cloud Data](https://mlsanity.com/paper/2303.17852/) (2023)
- [Tensor-based process control and monitoring for semiconductor manufacturing with unstable disturbances](https://mlsanity.com/paper/2401.17573/) (2024)
- [Generalized Nonnegative Structured Kruskal Tensor Regression](https://mlsanity.com/paper/2509.19900/) (2025)

## Abstract (arXiv)

Advanced 3D metrology technologies such as Coordinate Measuring Machine (CMM) and laser 3D scanners have facilitated the collection of massive point cloud data, beneficial for process monitoring, control and optimization. However, due to their high dimensionality and structure complexity, modeling and analysis of point clouds are still a challenge. In this paper, we utilize multilinear algebra techniques and propose a set of tensor regression approaches to model the variational patterns of point clouds and to link them to process variables. The performance of the proposed methods is evaluated through simulations and a real case study of turning process optimization.