@inproceedings{2450cc61281742c9962889cf49440e9e,
title = "Face recognition based on Low-Rank matrix Representation",
abstract = "Based on the recent success of Low-Rank matrix Representation (LRR), we propose a novel classification method for robust face recognition, named LRR-based Classification (LRRC). By the ideal that if each data class is linearly spanned by a subspace of unknown dimensions and the data are noiseless, the lowest-rank representations of a set of test vector samples with respect to a set of training vector samples have the nature of being both dense for within-class affinity and almost zero for between-class affinities. Consequently, the LRR exactly reveals the classification of the data. Our experimental results demonstrate that LRRC has competitive with state-of-the-art classification methods.",
keywords = "Classification, Face recognition, Feature extraction, Low rank representation, Sparse representation",
author = "Nguyen, \{Hoang Vu\} and Rong Huang and Wankou Yang and Changyin Sun",
note = "Publisher Copyright: {\textcopyright} 2014 TCCT, CAA.; Proceedings of the 33rd Chinese Control Conference, CCC 2014 ; Conference date: 28-07-2014 Through 30-07-2014",
year = "2014",
month = sep,
day = "11",
doi = "10.1109/ChiCC.2014.6895722",
language = "英语",
series = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",
publisher = "IEEE Computer Society",
pages = "4647--4652",
editor = "Shengyuan Xu and Qianchuan Zhao",
booktitle = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",
}