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

• URL: http://arxiv.org/abs/2207.11353v2
• Date: Mon, 5 Jun 2023 01:29:22 GMT
• Title: A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data
• Authors: Chengyu Zhou and Xiaolei Fang
• Abstract summary: This paper proposes a supervised dimension reduction methodology for tensor data which has two advantages over most image-based prognostic models. 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.
• Score: 2.538209532048867
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Dimension reduction via score ratio matching [0.9012198585960441]
  We propose a framework, derived from score-matching, to extend gradient-based dimension reduction to problems where gradients are unavailable. We show that our approach outperforms standard score-matching for problems with low-dimensional structure.
  arXiv  Detail & Related papers  (2024-10-25T22:21:03Z)
• Exploiting Diffusion Prior for Generalizable Dense Prediction [85.4563592053464]
  Recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf dense predictors to estimate. We introduce DMP, a pipeline utilizing pre-trained T2I models as a prior for dense prediction tasks. Despite limited-domain training data, the approach yields faithful estimations for arbitrary images, surpassing existing state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2023-11-30T18:59:44Z)
• Fine-grained Retrieval Prompt Tuning [149.9071858259279]
  Fine-grained Retrieval Prompt Tuning steers a frozen pre-trained model to perform the fine-grained retrieval task from the perspectives of sample prompt and feature adaptation. Our FRPT with fewer learnable parameters achieves the state-of-the-art performance on three widely-used fine-grained datasets.
  arXiv  Detail & Related papers  (2022-07-29T04:10:04Z)
• Noisy Tensor Completion via Low-rank Tensor Ring [41.86521269183527]
  tensor completion is a fundamental tool for incomplete data analysis, where the goal is to predict missing entries from partial observations. Existing methods often make the explicit or implicit assumption that the observed entries are noise-free to provide a theoretical guarantee of exact recovery of missing entries. This paper proposes a novel noisy tensor completion model, which complements the incompetence of existing works in handling the degeneration of high-order and noisy observations.
  arXiv  Detail & Related papers  (2022-03-14T14:09:43Z)
• Extension of Dynamic Mode Decomposition for dynamic systems with incomplete information based on t-model of optimal prediction [69.81996031777717]
  The Dynamic Mode Decomposition has proved to be a very efficient technique to study dynamic data. The application of this approach becomes problematic if the available data is incomplete because some dimensions of smaller scale either missing or unmeasured. We consider a first-order approximation of the Mori-Zwanzig decomposition, state the corresponding optimization problem and solve it with the gradient-based optimization method.
  arXiv  Detail & Related papers  (2022-02-23T11:23:59Z)
• PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty [15.781915567005251]
  We present a novel surrogate model for representation learning and uncertainty quantification. The proposed model combines a neural network approach for dimensionality reduction of the (potentially high-dimensional) data, with a surrogate model method for learning the data distribution. Our model enables us to (a) learn a representation of the data, (b) estimate uncertainty in the high-dimensional data system, and (c) match high order moments of the output distribution.
  arXiv  Detail & Related papers  (2022-02-10T14:42:51Z)
• A DCT-based Tensor Completion Approach for Recovering Color Images and Videos from Highly Undersampled Data [0.8399688944263843]
  We propose a novel tensor completion approach to recover color images and videos from undersampled data. A series of numerical experiments including color image inpainting and video data recovery demonstrate that our proposed approach performs better than many existing state-of-theart tensor completion methods.
  arXiv  Detail & Related papers  (2021-10-18T13:41:27Z)
• A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification [47.25472624305589]
  fuzzyc-regression clustering based on type-2 fuzzyset has been shown the remarkable results on non-sparse data. Aninnovative architecture for fuzzyc-regression model is presented and a novel student-tdistribution based membership functionis designed for sparse data modelling.
  arXiv  Detail & Related papers  (2020-09-02T05:10:13Z)
• Slice Sampling for General Completely Random Measures [74.24975039689893]
  We present a novel Markov chain Monte Carlo algorithm for posterior inference that adaptively sets the truncation level using auxiliary slice variables. The efficacy of the proposed algorithm is evaluated on several popular nonparametric models.
  arXiv  Detail & Related papers  (2020-06-24T17:53:53Z)
• Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering [50.43424130281065]
  We propose a new Semi-Nonnegative Matrix Factorization method for 2-dimensional (2D) data, named TS-NMF. It overcomes the drawback of existing methods that seriously damage the spatial information of the data by converting 2D data to vectors in a preprocessing step.
  arXiv  Detail & Related papers  (2020-05-19T05:54:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.