Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Projective Representation Learning for Discriminative Face Recognition Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Structured Multi-view Supervised Feature Selection Algorithm Research

Authors : Caijuan Shi, Li-li Zhao, Liping Liu, Jian Liu, Qi Tian

Published in: Computer Vision

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Face more and more multi-view data, how to enhance the feature selection performance has become one of the research issues. However, the most existing multi-view feature selection methods only consider the importance of each view features, but ignore the importance of individual feature in each view in the feature selection progress. In this paper we propose a novel supervised feature selection method based on structured multi-view sparse regularization, namely Structured Multi-view Supervised Feature Selection (SMSFS). SMSFS can realize feature selection by both considering the importance of each view features and the importance of individual feature in each view to boost the feature selection performance. Extensive experiments are performed on two image datasets and the results show the effectiveness of the proposed method SMSFS.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Unsupervised Multi-view Subspace Learning via Maximizing Dependence
next chapter An Automatic Shoeprint Retrieval Method Using Neural Codes for Commercial Shoeprint Scanners
Literature
1.
2.
Shi, C.J., Ruan, Q.Q., An, G.Y.: Sparse feature selection based on graph Laplacian for web image annotation. Image Vis. Comput. 32(3), 189–201 (2014)CrossRef
3.
Ma, Z.G., Nie, F.P., Yang, Y., Uijlings, J.R.R., Sebe, N.: Web image annotation via subspace-sparsity collaborated feature selection. IEEE Trans. Multimedia 14(4), 1021–1030 (2012)CrossRef
4.
Shi, C.J., Ruan, Q.Q., An, G.Y.: Semi-supervised sparse feature selection based on multi-view Laplacian regularization. Image Vis. Comput. 41(9), 1–10 (2015)CrossRef
5.
Wang, H., Nie, F., Huang, H., Risacher, S.L., Saykin, A.J., Shen, L., et al.: Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning. Bioinformatics 28(12), i127–i136 (2012)CrossRef
6.
Wang, H., Nie, F., Huang, H., Ding, C.: Heterogeneous visual features fusion via sparse multimodal machine. In: Proceedings of CVPR, pp. 3097–3102 (2013)
7.
Chun, T., Tang, J., Hong, R., et al.: NUS-WIDE: a real-world web image dataset from National University of Singapore. In: Proceedings of CIVR, pp. 1–9 (2009)
8.
Li, H., Wang, M., Hua, X.: MSRA-MM2.0: a large-scale web multimedia dataset. In: Proceedings of ICDMW, pp. 164–169 (2009)
9.
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Workshop on Computational Learning Theory, pp. 92–100 (1998)
10.
Hotelling, H.: Relations between two sets of variates. Biometrika 28(3/4), 321–377 (1936)CrossRefMATH
11.
Akaho, S.: A kernel method for canonical correlation analysis, Arxiv preprint cs/0609071 (2006)
12.
Nilufar, S., Ray, N., Zhang, H.: Object detection with DOG scale space: a multiple kernel learning approach. IEEE Trans. Image Process. 21(8), 3744–3756 (2012)MathSciNetCrossRefMATH
13.
Cawley, G., Talbot, N., Girolami, M.: Sparse multinomial logistic regression via bayesian \(L_1\) regularisation. In: Proceedings of NIPS, pp. 209–216 (2006)
14.
Chartrand, R.: Exact reconstruction of sparse signals via nonconvex minimizaion. IEEE Sig. Process. Lett. 14(10), 707–710 (2007)CrossRef
15.
Chartrand, R.: Fast algorithms for nonconvex compressive sensing: MRI reconstruction from very few data. In: Proceedings of IEEE International Symposium on Biomedical Imaging, pp. 262–265 (2009)
16.
Xu, Z.B., Zhang, H., Wang, Y., Chang, X.Y., Liang, Y.: \(L_{1/2}\) regularizer. Sci. China. 53(6), 1159–1169 (2010)MathSciNet
17.
Nie, F.P., Xu, D., Hung, T., Zhang, C.: Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction. IEEE Trans. Image Process. 19(7), 1921–1932 (2010)MathSciNetCrossRefMATH
18.
Metadata
Title
Structured Multi-view Supervised Feature Selection Algorithm Research
Authors
Caijuan Shi
Li-li Zhao
Liping Liu
Jian Liu
Qi Tian
Copyright Year
2017
Publisher
Springer Singapore
Book
Computer Vision

Print ISBN: 978-981-10-7301-4

Electronic ISBN: 978-981-10-7302-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-7302-1_13

Premium Partner

    Image Credits