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

Chengyu Zhou, Xiaolei Fang
7/22/2022
cs.LGeess.IVstat.ML

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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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Cite this paper

@article{zhou2022supervised,
  title  = {A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data},
  author = {Chengyu Zhou and Xiaolei Fang},
  year   = {2022},
  eprint = {2207.11353},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.11353v2}
}

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