Semi-supervised low-rank representation for image classification

Chenxue Yang; Mao Ye; Song Tang; Tao Xiang; Zijian Liu

doi:10.1007/s11760-016-0895-4

ScienceGate Book Chapters

JOURNAL ARTICLE

Semi-supervised low-rank representation for image classification

Chenxue Yang Mao Ye Song Tang Tao Xiang Zijian Liu

Year: 2016 Journal: Signal Image and Video Processing Vol: 11 (1)Pages: 73-80 Publisher: Springer Science+Business Media

DOI: 10.1007/s11760-016-0895-4

Get Full-Text PDF Get Analytical Report

Keywords:

Rank (graph theory) Pattern recognition (psychology) Graph Constraint (computer-aided design) Representation (politics) Computer science Sparse approximation Artificial intelligence Set (abstract data type) Image (mathematics) Data mining Machine learning Mathematics Theoretical computer science

Metrics

Cited By

2.11

FWCI (Field Weighted Citation Impact)

Refs

0.86

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Sparse and Compressive Sensing Techniques

Physical Sciences → Engineering → Computational Mechanics

Remote-Sensing Image Classification

Physical Sciences → Engineering → Media Technology

Advanced Image and Video Retrieval Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Semi-supervised low-rank representation for image classification

Metrics

Citation History

Topics

Related Documents

Semi-Supervised hyperspectral image classification using local low-rank representation

Semi-Supervised Classification Based on Low Rank Representation

Semi-supervised classification via kernel low-rank representation graph

Classification and saliency detection by semi-supervised low-rank representation

Self-Supervised Low-Rank Representation (SSLRR) for Hyperspectral Image Classification