Structure preserving low-rank representation for semi-supervised face recognition

Yong Peng, Suhang Wang, Shen Wang, Bao Liang Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Constructing an informative and discriminative graph plays an important role in the graph based semi-supervised learning methods. Among these graph construction methods, low-rank representation based graph, which calculates the edge weights of both labeled and unlabeled samples as the low-rank representation (LRR) coefficients, has shown excellent performance in semi-supervised learning. In this paper, we additionally impose twofold constraints (local affinity and distant repulsion) on the LRR graph. The improved model, termed structure preserving LRR (SPLRR), can preserve the local geometrical structure but without distorting the distant repulsion property. Experiments are taken on three widely used face data sets to investigate the performance of SPLRR and the results show that it is superior to some state-of-the-art semi-supervised graphs.

Original languageEnglish (US)
Title of host publicationNeural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
Number of pages8
EditionPART 2
StatePublished - 2013
Event20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, Korea, Republic of
Duration: Nov 3 2013Nov 7 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8227 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Neural Information Processing, ICONIP 2013
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Structure preserving low-rank representation for semi-supervised face recognition'. Together they form a unique fingerprint.

Cite this