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

arXiv: https://arxiv.org/abs/2010.06784 · HTML: https://mlsanity.com/paper/2010.06784/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Embedded Deep Regularized Block HSIC Thermomics for Early Diagnosis of Breast Cancer](https://mlsanity.com/paper/2106.02106/) (2021)
- [Distribution-based Low-rank Embedding](https://mlsanity.com/paper/2312.17579/) (2023)

## Abstract (arXiv)

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%).