# A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data

arXiv: https://arxiv.org/abs/2207.11353 · HTML: https://mlsanity.com/paper/2207.11353/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 31 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Built on by (most cited first, 5 of 5)

- [Federated Multilinear Principal Component Analysis with Applications in Prognostics](https://mlsanity.com/paper/2312.06050/) (2023)
- [Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes](https://mlsanity.com/paper/2405.06068/) (2024)
- [A Federated Data Fusion-Based Prognostic Model for Applications with Multi-Stream Incomplete Signals](https://mlsanity.com/paper/2311.07474/) (2023)
- [Differentially Private Log-Location-Scale Regression Using Functional Mechanism](https://mlsanity.com/paper/2404.08715/) (2024)
- [Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics](https://mlsanity.com/paper/2604.19451/) (2026)

## Abstract (arXiv)

This paper proposes a supervised dimension reduction methodology for tensor data which has two advantages over most image-based prognostic models. First, the model does not require tensor data to be complete which expands its application to incomplete data. Second, it utilizes time-to-failure (TTF) to supervise the extraction of low-dimensional features which makes the extracted features more effective for the subsequent prognostic. Besides, an optimization algorithm is proposed for parameter estimation and closed-form solutions are derived under certain distributions.