Orthogonal self-guided similarity preserving projections

Xiaozhao Fang, Yong Xu, Zheng Zhang, Zhihui Lai, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we propose a novel unsupervised dimensionality reduction (DR) method called orthogonal self-guided similarity preserving projections (OSSPP), which seamlessly integrates the procedures of an adjacency graph learning and DR into a one step. Specifically, OSSPP projects the data into a low-dimensional subspace and simultaneously performs similarity preserving learning by using the similarity preserving regularization term in which the reconstruction coefficients of the projected data are used to encode the similarity structure information. An interesting finding is that the problem to determine the reconstruction coefficients can be converted into a weighted non-negative sparse coding problem without any explicit sparsity constraint. Thus the projections obtained by OSSPP contain natural discriminating information. Experimental results demonstrate that OSSPP outperforms state-of-the-art methods in DR.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Pages344-348
Number of pages5
ISBN (Electronic)9781479983391
DOIs
Publication statusPublished - 9 Dec 2015
Externally publishedYes
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 27 Sep 201530 Sep 2015

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2015-December
ISSN (Print)1522-4880

Conference

ConferenceIEEE International Conference on Image Processing, ICIP 2015
Country/TerritoryCanada
CityQuebec City
Period27/09/1530/09/15

Keywords

  • dimensionality reduction
  • similarity preserving
  • sparse coding

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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