论文标题
具有不完整成像数据的应用的监督张量缩小尺寸的预后模型
A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data
论文作者
论文摘要
本文提出了一种张量数据的监督尺寸减小方法，该方法比大多数基于图像的预后模型具有两个优点。首先，该模型不需要张量数据完成，从而将其应用程序扩展到不完整的数据。其次，它利用时间进行失败（TTF）来监督低维特征的提取，这使提取的特征对后续预后更有效。此外，提出了一种优化算法以进行参数估计，并在某些分布中得出了封闭形式的解决方案。
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.
