Low-rank Convex/Sparse Thermal Matrix Approximation for Infrared-based Diagnostic System

• URL: http://arxiv.org/abs/2010.06784v1
• Date: Wed, 14 Oct 2020 02:53:19 GMT
• Title: Low-rank Convex/Sparse Thermal Matrix Approximation for Infrared-based Diagnostic System
• Authors: Bardia Yousefi, Clemente Ibarra Castanedo, Xavier P.V. Maldague
• Abstract summary: This study conducts a comparative analysis on low-rank matrix approximation methods in thermography with applications of semi-, convex, and sparse- non-negative matrix factorization (NMF) methods for detecting subsurface thermal patterns. Results show that these methods inherit the advantages of principal component thermography (PCT) and sparse PCT, whereas tackle negative bases in sparse PCT with non-negative constraints, and exhibit clustering property in processing data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Active and passive thermography are two efficient techniques extensively used to measure heterogeneous thermal patterns leading to subsurface defects for diagnostic evaluations. This study conducts a comparative analysis on low-rank matrix approximation methods in thermography with applications of semi-, convex-, and sparse- non-negative matrix factorization (NMF) methods for detecting subsurface thermal patterns. These methods inherit the advantages of principal component thermography (PCT) and sparse PCT, whereas tackle negative bases in sparse PCT with non-negative constraints, and exhibit clustering property in processing data. The practicality and efficiency of these methods are demonstrated by the experimental results for subsurface defect detection in three specimens (for different depth and size defects) and preserving thermal heterogeneity for distinguishing breast abnormality in breast cancer screening dataset (accuracy of 74.1%, 75.8%, and 77.8%).

Related papers

• Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models [50.90868087591973]
  We propose an approximate Bayesian method for quantifying the total uncertainty in inverse PDE solutions obtained with machine learning surrogate models. We test the proposed framework by comparing it with the iterative ensemble smoother and deep ensembling methods for a non-linear diffusion equation.
  arXiv  Detail & Related papers  (2024-08-20T19:06:02Z)
• Machine Learning-based Layer-wise Detection of Overheating Anomaly in LPBF using Photodiode Data [0.0]
  This research focuses on the detection of overheating anomalies using photodiode sensor data. Photodiode sensors can collect high-frequency data from the melt pool, reflecting the process dynamics and thermal history. The proposed method offers a machine learning framework to utilize photodiode sensor data for layer-wise detection of overheating anomalies.
  arXiv  Detail & Related papers  (2024-03-20T01:12:44Z)
• Distribution-based Low-rank Embedding [0.0]
  We propose James-Stein for eigenvector (JSE) and Weibull embedding approaches, as two novel strategies in computational thermography. The primary objective is to create a low-dimensional (LD) representation of the thermal data stream. The results of the proposed method indicate an enhancement in the projection of the predominant basis vector, yielding classification accuracy of 81.7%.
  arXiv  Detail & Related papers  (2023-12-29T12:29:45Z)
• A Novel Maximum-Entropy-Driven Technique for Low-Rank Orthogonal Nonnegative Matrix Factorization with $\ell_0$-Norm sparsity Constraint [0.0]
  In data-driven control and machine learning, a common requirement involves breaking down large matrices into smaller, low-rank factors. This paper introduces an innovative solution to the orthogonal nonnegative matrix factorization (ONMF) problem. The proposed method achieves comparable or improved reconstruction errors in line with the literature.
  arXiv  Detail & Related papers  (2022-10-06T04:30:59Z)
• Off-policy estimation of linear functionals: Non-asymptotic theory for semi-parametric efficiency [59.48096489854697]
  The problem of estimating a linear functional based on observational data is canonical in both the causal inference and bandit literatures. We prove non-asymptotic upper bounds on the mean-squared error of such procedures. We establish its instance-dependent optimality in finite samples via matching non-asymptotic local minimax lower bounds.
  arXiv  Detail & Related papers  (2022-09-26T23:50:55Z)
• Unsupervised Learning Discriminative MIG Detectors in Nonhomogeneous Clutter [0.8984888893275712]
  Principal component analysis (PCA) maps high-dimensional data into a lower-dimensional space maximizing the data variance. Inspired by the principle of PCA, a novel type of learning discriminative matrix information geometry (MIG) detectors are developed. Three discriminative MIG detectors are illustrated with respect to different geometric measures.
  arXiv  Detail & Related papers  (2022-04-24T13:50:05Z)
• Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction [108.06731611196291]
  We develop a multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies. We propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features.
  arXiv  Detail & Related papers  (2022-03-10T14:22:54Z)
• Improving Metric Dimensionality Reduction with Distributed Topology [68.8204255655161]
  DIPOLE is a dimensionality-reduction post-processing step that corrects an initial embedding by minimizing a loss functional with both a local, metric term and a global, topological term. We observe that DIPOLE outperforms popular methods like UMAP, t-SNE, and Isomap on a number of popular datasets.
  arXiv  Detail & Related papers  (2021-06-14T17:19:44Z)
• Embedded Deep Regularized Block HSIC Thermomics for Early Diagnosis of Breast Cancer [0.0]
  Matrix factorization (MF) techniques can detect thermal patterns corresponding to vasodilation in cancer cases. One of the biggest challenges in such techniques is selecting the best representation of the thermal basis. Deep-semi-nonnegative matrix factorization (Deep-SemiNMF) for thermography is introduced, then tested for 208 breast cancer screening cases.
  arXiv  Detail & Related papers  (2021-06-03T19:54:31Z)
• Entropy Minimizing Matrix Factorization [102.26446204624885]
  Nonnegative Matrix Factorization (NMF) is a widely-used data analysis technique, and has yielded impressive results in many real-world tasks. In this study, an Entropy Minimizing Matrix Factorization framework (EMMF) is developed to tackle the above problem. Considering that the outliers are usually much less than the normal samples, a new entropy loss function is established for matrix factorization.
  arXiv  Detail & Related papers  (2021-03-24T21:08:43Z)
• Robust Low-rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method [47.80060761046752]
  Robust low-rank matrix completion (RMC) has been studied extensively for computer vision, signal processing and machine learning applications. This problem aims to decompose a partially observed matrix into the superposition of a low-rank matrix and a sparse matrix, where the sparse matrix captures the grossly corrupted entries of the matrix. A widely used approach to tackle RMC is to consider a convex formulation, which minimizes the nuclear norm of the low-rank matrix (to promote low-rankness) and the l1 norm of the sparse matrix (to promote sparsity). In this paper, motivated by some recent works on low-
  arXiv  Detail & Related papers  (2020-08-18T04:46:22Z)

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.