Manifold adaptive kernelized low-rank representation for semisupervised image classification
DOI10.1155/2018/2857594zbMath1390.68738OpenAlexW2804115677MaRDI QIDQ1649496
Yong Peng, Feiping Nie, Feiwei Qin, Wanzeng Kong
Publication date: 6 July 2018
Published in: Complexity (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1155/2018/2857594
nonlinearitygraph-based semisupervised learning algorithmslow-rank representation (LRR)manifold kernelized low-rank representation (MKLRR) modelsemisupervised image classification
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning
- Robust subspace segmentation via nonconvex low rank representation
- A cellular automaton model based on empirical observations of a driver's oscillation behavior reproducing the findings from Kerner's three-phase traffic theory
- Finding the homology of submanifolds with high confidence from random samples
- Robust principal component analysis?
- A Singular Value Thresholding Algorithm for Matrix Completion
- Learning with $\ell ^{1}$-graph for image analysis
- Graph Regularized Sparse Coding for Image Representation
This page was built for publication: Manifold adaptive kernelized low-rank representation for semisupervised image classification