Next Article in Journal
The Resistance–Amplitude–Frequency Effect of In–Liquid Quartz Crystal Microbalance
Next Article in Special Issue
Energy Harvesting Based Body Area Networks for Smart Health
Previous Article in Journal
Automatic Detection and Classification of Pole-Like Objects for Urban Cartography Using Mobile Laser Scanning Data
Previous Article in Special Issue
Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(7), 1475; doi:10.3390/s17071475

Block-Diagonal Constrained Low-Rank and Sparse Graph for Discriminant Analysis of Image Data

College of Communication Engineering, Chongqing University, Chongqing 400044, China
*
Authors to whom correspondence should be addressed.
Received: 29 March 2017 / Revised: 14 June 2017 / Accepted: 19 June 2017 / Published: 22 June 2017
View Full-Text   |   Download PDF [3528 KB, uploaded 23 June 2017]   |  

Abstract

Recently, low-rank and sparse model-based dimensionality reduction (DR) methods have aroused lots of interest. In this paper, we propose an effective supervised DR technique named block-diagonal constrained low-rank and sparse-based embedding (BLSE). BLSE has two steps, i.e., block-diagonal constrained low-rank and sparse representation (BLSR) and block-diagonal constrained low-rank and sparse graph embedding (BLSGE). Firstly, the BLSR model is developed to reveal the intrinsic intra-class and inter-class adjacent relationships as well as the local neighborhood relations and global structure of data. Particularly, there are mainly three items considered in BLSR. First, a sparse constraint is required to discover the local data structure. Second, a low-rank criterion is incorporated to capture the global structure in data. Third, a block-diagonal regularization is imposed on the representation to promote discrimination between different classes. Based on BLSR, informative and discriminative intra-class and inter-class graphs are constructed. With the graphs, BLSGE seeks a low-dimensional embedding subspace by simultaneously minimizing the intra-class scatter and maximizing the inter-class scatter. Experiments on public benchmark face and object image datasets demonstrate the effectiveness of the proposed approach. View Full-Text
Keywords: dimensionality reduction; low-rank representation; sparse representation; block-diagonal; image classification dimensionality reduction; low-rank representation; sparse representation; block-diagonal; image classification
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Guo, T.; Tan, X.; Zhang, L.; Xie, C.; Deng, L. Block-Diagonal Constrained Low-Rank and Sparse Graph for Discriminant Analysis of Image Data. Sensors 2017, 17, 1475.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top