A comparative Study of Low-Rank-plus-Sparse Matrix Decomposition and Machine Learning for Non-Destructive Air-Ultrasound Defect Detection.
Manar Al-AskaryUdaya Sampath K. Perera Miriya ThanthrigePierre PfefferLuis WachterGiovanni SchoberAydin SezginPublished in: EUSIPCO (2021)
Keyphrases
- matrix decomposition
- low rank
- defect detection
- machine learning
- low rank matrix
- rank minimization
- singular value decomposition
- convex optimization
- missing data
- low rank approximation
- linear combination
- matrix factorization
- high order
- high dimensional data
- kernel matrix
- semi supervised
- feature extraction
- active learning
- nonnegative matrix factorization
- singular values
- data matrix
- feature selection
- learning tasks
- pattern recognition
- learning algorithm
- eigendecomposition
- data mining
- missing values
- text mining
- nearest neighbor
- approximation methods
- sparse matrix
- computer vision