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

2022/07/22 by Chengyu Zhou, Zhou, Chengyu, Xiaolei Fang +1
Computer Science · Medicine · #Advanced Neural Network Applications #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Image Segmentation Techniques #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2207.11353

openalex publication_date 2022/07/22 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28

Abstract

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.

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