Next Article in Journal
ANFIS Based Time Series Prediction Method of Bank Cash Flow Optimized by Adaptive Population Activity PSO Algorithm
Previous Article in Journal
ODQ: A Fluid Office Document Query Language
Article Menu

Export Article

Open AccessArticle
Information 2015, 6(3), 287-299; doi:10.3390/info6030287

Robust Sparse Representation for Incomplete and Noisy Data

School of Science, Xi'an University of Architecture and Technology, Xi'an 710055, China
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Received: 12 June 2015 / Revised: 15 June 2015 / Accepted: 16 June 2015 / Published: 24 June 2015
(This article belongs to the Section Information Applications)
View Full-Text   |   Download PDF [1032 KB, uploaded 24 June 2015]   |  


Owing to the robustness of large sparse corruptions and the discrimination of class labels, sparse signal representation has been one of the most advanced techniques in the fields of pattern classification, computer vision, machine learning and so on. This paper investigates the problem of robust face classification when a test sample has missing values. Firstly, we propose a classification method based on the incomplete sparse representation. This representation is boiled down to an l1 minimization problem and an alternating direction method of multipliers is employed to solve it. Then, we provide a convergent analysis and a model extension on incomplete sparse representation. Finally, we conduct experiments on two real-world face datasets and compare the proposed method with the nearest neighbor classifier and the sparse representation-based classification. The experimental results demonstrate that the proposed method has the superiority in classification accuracy, completion of the missing entries and recovery of noise. View Full-Text
Keywords: sparse representation; robust; face classification; alternating direction method of multipliers; incomplete; l1 minimization sparse representation; robust; face classification; alternating direction method of multipliers; incomplete; l1 minimization

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

Shi, J.; Zheng, X.; Yang, W. Robust Sparse Representation for Incomplete and Noisy Data. Information 2015, 6, 287-299.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top