research-article

Semi-Supervised Image Classification by Nonnegative Sparse Neighborhood Propagation

Authors:

Fanzhang LiAuthors Info & Claims

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

Pages 139 - 146

https://doi.org/10.1145/2671188.2749292

Published: 22 June 2015 Publication History

Abstract

This paper proposes an enhanced semi-supervised classification approach termed Nonnegative Sparse Neighborhood Propagation (SparseNP) that is an improvement to the existing neighborhood propagation due to the fact that the outputted soft labels of points cannot be ensured to be sufficiently sparse, discriminative, robust to noise and be probabilistic values. Note that the sparse property and strong discriminating ability of predicted labels is important, since ideally the soft label of each sample should have only one or few positive elements (that is, less unfavorable mixed signs are included) deciding its class assignment. To reduce the negative effects of unfavorable mixed signs on the learning performance, we regularize the l_2,1-norm on the soft labels during optimization for enhancing the prediction results. The non-negativity and sum-to-one constraints are also included to ensure the outputted labels are probabilistic values. The proposed framework is solved in an alternative manner for delivering a more reliable solution so that the accuracy can be improved. Simulations show that satisfactory results can be obtained by the proposed SparseNP compared with other related approaches.

References

[1]

O. Chapelle, B. Scholkopf, and A. Zien, "Semi-Supervised Learning," Cambridge: MIT Press, 2006.

[2]

X. Zhu, "Semi-supervised learning literature survey," Technical Report 1530, Univ. Wisconsin-Madison. 2005.

[3]

D. Zhou, O. Bousquet, T. N. Lal, J. Weston, B. S cholkopf, "Learning with local and global consistency," In: Proc. Advances in Neural Information Processing Systems, 2004.

[4]

X. Zhu, Z. Ghahramani, and J. D. Lafferty, "Semi-supervised learning using Gaussian fields and harmonic functions. In: Proceedings of the International Conference on Machine Learning, 2003.

[5]

F. Wang, and C. S. Zhang, "Label propagation through linear Neighborhoods," IEEE Trans. on Knowledge and Data Engineering, vol. 20, no. 1, pp. 55--67, 2008.

Digital Library

[6]

Z. Zhang, M. B. Zhao, and T. W. S. Chow, "Graph based Constrained Semi-Supervised Learning Framework via Label Propagation over Adaptive Neighborhood," IEEE Trans. on Knowledge and Data Engineering, Dec 2013. TKDE.2013.182.

[7]

F. P. Nie, S. M. Xiang, Y. Liu, and C. S. Zhang, "A general graph-based semi-supervised learning with novel class discovery," Neural Computing Applications, vol. 19, no. 4, pp. 549--555, 2010.

Digital Library

[8]

J. Yang, A. F. Frangi, J.Y. Yang, D. Zhang, and J. Zhong, "KPCA plus LDA: a Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition," IEEE Trans. on Pattern analysis and machine intelligence, vol. 27, no. 2, pp. 230--244, 2005.

Digital Library

[9]

M. Turk, A. Pentland, "Face recognition using eigenfaces," In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 1991.

[10]

F. Zang, and J. S. Zhang, "Label propagation through sparse neighborhood and its applications, "Neurocomputing, vol. 97, pp. 267--277, 2012.

Digital Library

[11]

Y. Liu, F. P. Nie, J. G. Wu, and L. H. Chen, "Semi-supervised feature selection based on label propagation and subset selection," In: Proceedings of the International Conference on Computer and Information Application, 2010.

[12]

Z. Zhang, T. Chow, M. Zhao, "Trace Ratio Optimization based Semi-Supervised Nonlinear Dimensionality Reduction for Marginal Manifold Visualization," IEEE Transactions on Knowledge and Data Engineering, vol. 25, iss. 5, pp. 1148--1161, 2013.

Digital Library

[13]

H. Cheng, Z. Liu, and J. Yang, "Sparsity induced similarity measure for label propagation," In: Proceedings of the IEEE International Conference on Computer Vision, pp. 317--324, 2009.

[14]

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. Huang, S. C. Yan, "Sparse Representation for Computer Vision and Pattern Recognition," Proceedings of the IEEE, vol. 98, no. 6, pp. 1031--1044, 2010.

[15]

T. Sim, S. Baker, M. Bsat, "The Carnegie Mellon University pose, illuminlation, and expression database," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1615--1618, 2003.

Digital Library

[16]

Z. Zhang, M. B. Zhao, and T. W. S. Chow, "Binary- and Multi-Class Group Sparse Canonical Correlation Analysis for Feature Extraction and Classification," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 10, pp. 2192--2205, October 2013.

Digital Library

[17]

C. P. Hou, F. P. Nie, D. Y. Yi, Yi Wu, "Feature Selection via Joint Embedding Learning and Sparse Regression," In: Proceedings of the International Joint Conferences on Artificial Intelligence, pp. 1324--1329, 2011.

Digital Library

[18]

L. S. Qiao, S. C. Chen, and X. Y. Tan, "Sparsity Preserving Projections with Applications to Face Recognition," Pattern Recognition, vol. 43, no. 1, pp. 331--341, 2010.

Digital Library

[19]

Z. Zhang, S. C. Yan, and M. B. Zhao, "Pairwise Sparsity Preserving Embedding for Unsupervised Subspace Learning and Classification," IEEE Transactions on Image Processing, vol. 22, iss. 12, pp. 4640--4651, 2013.

Digital Library

[20]

D. B. Graham, and N. M. Allinson, "Characterizing virtual eigensignatures for general 870 purpose face recognition in face recognition: from theory to application," NATO ASI Series F, Computer and Systems Sciences, Vol. 163; H. Wechsler, P. J. Phillips, V. Bruce, F. Fogelman-Soulie and T. S. Huang (eds), pp. 446--456, 1998.

[21]

S. Roweis and L. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, no. 5500, pp. 2323--2326, 2000.

[22]

Y. Yang, H. T. Shen, Z. G. Ma, Z. Huang, and X. F. Zhou, "L_{2, 1}-Norm Regularized Discriminative Feature Selection for Unsupervised Learning," In: Proceeding of the International Joint Conferences on Artificial Intelligence, 2011.

Digital Library

[23]

E. Kokiopoulou, J. Chen, and Y. Saad, "Trace optimization and eigenproblems in dimension reduction methods," Numerical Linear Algebra with Applications, vol. 18, no. 3, pp. 565--602, 2011.

[24]

F. Nie, H. Huang, X. Cai, and C. Ding, "Efficient and robust feature selection via joint l_2,1-norms minimization," In: Proceeding of the Neural Information Processing Systems (NIPS), 2010.

[25]

M. Culp, and G. Michailidis, "Graph-based semi-supervised learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 1, pp. 174--179, 2008.

Digital Library

[26]

Z. Zhang, T. W. S. Chow, "Maximum Margin Multisurface Support Tensor Machines with Application to Image Classification and Segmentation," Expert Systems with Applications, vol. 39, iss. 1, pp. 850--861, 2012.

Digital Library

[27]

N. Dalal, and B. Triggs, "Histograms of Oriented Gradients for Human Detection," Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 886--893, 2005.

Digital Library

[28]

M. Belkin, and P. Niyogi, "Laplacian Eigenmaps for Dimensionality Reduction and Data Representation," Neural Computation, vol. 15, no. 6, pp. 1373--1396, 2003.

Digital Library

[29]

S. A. Nene, S. K. Nayar, and H. Murase, "Columbia Object Image Library (COIL-20)," Technical Report CUCS-005-96, 1996.

Cited By

Hua ZYang Y(2022)Robust and sparse label propagation for graph-based semi-supervised classificationApplied Intelligence10.1007/s10489-021-02360-z52:3(3337-3351)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10489-021-02360-z
Zhang HZhang ZZhao MYe QZhang MWang M(2020)Robust Triple-Matrix-Recovery-Based Auto-Weighted Label Propagation for ClassificationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2019.295601531:11(4538-4552)Online publication date: Nov-2020
https://doi.org/10.1109/TNNLS.2019.2956015
Zhang ZZhang YLiu GTang JYan SWang M(2020)Joint Label Prediction Based Semi-Supervised Adaptive Concept Factorization for Robust Data RepresentationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.289395632:5(952-970)Online publication date: 1-May-2020
https://doi.org/10.1109/TKDE.2019.2893956
Show More Cited By

Index Terms

Semi-Supervised Image Classification by Nonnegative Sparse Neighborhood Propagation
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Recommendations

Semi-supervised partial label learning algorithm via reliable label propagation
Abstract
Partial label learning (PLL) is a weakly supervised learning method that is able to predict one label as the correct answer from a given candidate label set. In PLL, when all possible candidate labels are as signed to real-world training examples, ...
Semi-supervised Bi-dictionary Learning Using Smooth Representation-Based Label Propagation
CYBERC '15: Proceedings of the 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

Due to heavy clutters and occlusions of complex background, natural images contain complex features in data structure which often cause errors in image classification. In this paper, we propose semi-supervised bi-dictionary learning for image ...
Robust and sparse label propagation for graph-based semi-supervised classification
Abstract
Traditional graph-based semi-supervised classification algorithms are usually composed of two independent parts: graph construction and label propagation. However, the predefined graph may not be optimal for label propagation, and these methods ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

June 2015

700 pages

ISBN:9781450332743

DOI:10.1145/2671188

General Chairs:
Alex Hauptmann
Carnegie Mellon University, USA
,
Chong-Wah Ngo
City University of Hong Kong, China
,
Xiangyang Xue
Fudan University, China
,
Program Chairs:
Yu-Gang Jiang
Fudan University, China
,
Cees Snoek
University of Amsterdam and Qualcomm Research Netherlands
,
Nuno Vasconcelos
University of California, San Diego, USA

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province of China

Conference

ICMR '15

Sponsor:

SIGMM

ICMR '15: International Conference on Multimedia Retrieval

June 23 - 26, 2015

Shanghai, China

Acceptance Rates

ICMR '15 Paper Acceptance Rate 48 of 127 submissions, 38%;

Overall Acceptance Rate 254 of 830 submissions, 31%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

28
Total Citations
View Citations
195
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Hua ZYang Y(2022)Robust and sparse label propagation for graph-based semi-supervised classificationApplied Intelligence10.1007/s10489-021-02360-z52:3(3337-3351)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10489-021-02360-z
Zhang HZhang ZZhao MYe QZhang MWang M(2020)Robust Triple-Matrix-Recovery-Based Auto-Weighted Label Propagation for ClassificationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2019.295601531:11(4538-4552)Online publication date: Nov-2020
https://doi.org/10.1109/TNNLS.2019.2956015
Zhang ZZhang YLiu GTang JYan SWang M(2020)Joint Label Prediction Based Semi-Supervised Adaptive Concept Factorization for Robust Data RepresentationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.289395632:5(952-970)Online publication date: 1-May-2020
https://doi.org/10.1109/TKDE.2019.2893956
Ren JZhang ZLi SWang YLiu GYan SWang M(2020)Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed SpaceIEEE Transactions on Image Processing10.1109/TIP.2020.296528929(3941-3956)Online publication date: 2020
https://doi.org/10.1109/TIP.2020.2965289
Zhang ZRen JJiang WZhang ZHong RYan SWang M(2020)Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary LearningIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2019.292300730:8(2430-2446)Online publication date: 1-Aug-2020
https://dl.acm.org/doi/10.1109/TCSVT.2019.2923007
Xie Y(2020)Modified Label Propagation on Manifold With Applications to Fault ClassificationIEEE Access10.1109/ACCESS.2020.29953998(97771-97782)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2995399
Teng XLan LZhang XDong GLuo Z(2020)Transductive Nonnegative Matrix Tri-FactorizationIEEE Access10.1109/ACCESS.2020.29895278(81331-81347)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2989527
Jia LZhang HZhang Z(2020)Adaptive Multiple-View Label Propagation for Semi-supervised ClassificationNeural Computing for Advanced Applications10.1007/978-981-15-7670-6_1(1-11)Online publication date: 13-Aug-2020
https://doi.org/10.1007/978-981-15-7670-6_1
Zhang ZJiang WZhang ZLi SLiu GQin J(2019)Scalable block-diagonal locality-constrained projective dictionary learningProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367650(4376-4382)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367471.3367650
Du BXinyao TWang ZZhang LTao D(2019)Robust Graph-Based Semisupervised Learning for Noisy Labeled Data via Maximum Correntropy CriterionIEEE Transactions on Cybernetics10.1109/TCYB.2018.280432649:4(1440-1453)Online publication date: Apr-2019
https://doi.org/10.1109/TCYB.2018.2804326
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten