Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7-8/2014

01.06.2014 | Original Article

RETRACTED ARTICLE: Sparse tensor CCA for color face recognition

verfasst von: Shucheng Huang, Jian Chen, Zhi Luo

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2014

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This paper proposes a subspace learning method, named as sparse tensor canonical correlation analysis (ST-CCA), for color face recognition. A sample image is formalized as high-order tensors to preserve the inherent structure of the color face images. We utilize sparse canonical correlation analysis (SCCA) to choose gene. For each pair of tensors, SCCA generates the sparse loadings alternately, which is helpful for choosing significant variables to reduce dimensions and eliminate the redundancies of tensors. We use the elastic net as constraint condition to attack the collinearity problem by decorrelating and selecting the sufficient variables irrespective of the limited dimensions. Furthermore, ST-CCA gains stable recognition rates because the alternating least square solution converges. ST-CCA is convex with different initials of the projection matrices. Experimental results on AR face database and LFW face database show the superior performance of our method over the state-of-the-art ones.
Vorheriger Artikel Observer-based control for time-varying delay neural networks with nonlinear observation
Nächster Artikel Discrete cuckoo search algorithm for the travelling salesman problem

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Literatur
1.
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRef
2.
Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces versus fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720. doi:10.​1109/​34.​598228 CrossRef
3.
Hotelling H (1936) Relations between two sets of variates. Biometrika 28:312–377CrossRef
4.
Yang J, Zhang D, Frangi AF, Yang JY (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137CrossRef
5.
Li M, Yuan BZ (2005) 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recogn Lett 26(5):527–532CrossRef
6.
Torres L, Reutter , Lorente L (1999) The importance of the color information in face recognition. In: Proceedings 1999 international conference on image processing, 1999. ICIP 99. vol. 3, IEEE, pp. 627–631
7.
Wang C, Yin B, Bai X, Sun Y (2008) Color face recognition based on 2DPCA. In: 19th international conference on pattern recognition, ICPR. pp. 1–4
8.
Choi J, Ro Y, Plataniotis K (2009) Color face recognition for degraded face images. IEEE Trans Syst Man Cybern B Cybern 39(5):1217–1230CrossRef
9.
10.
Liu C (2008) Learning the uncorrelated, independent, and discriminating color spaces for face recognition. IEEE Trans Inf Forensics Secur 3(2):213–222CrossRef
11.
Yang J, Liu C (2008) Color image discriminant models and algorithms for face recognition. IEEE Trans Neural Networks 19(12):2088–2098CrossRef
12.
13.
Zou H, Hastie T, Tibshirani R (2006) Sparse principal component analysis. J Comput Graph stat 15(2):265–286MathSciNetCrossRef
14.
15.
Zou H, Hastie T Regression shrinkage and selection via the elastic net, with applications to microarrays, JR Statist. Soc. B
16.
Moghaddam B, Weiss Y, Avidan S (2006) Spectral bounds for sparse PCA: exact and greedy algorithms. Adv Neural Inf Process Syst 18:915
17.
Moghaddam B, Weiss Y, Avidan S (2006) Generalized spectral bounds for sparse LDA. In: Proceedings of the 23rd international conference on machine learning, ACM, pp. 641–648
18.
Qiao Z, Zhou L, Huang JZ Sparse linear discriminant analysis with applications to high dimensional low sample size data, IAENG. Int J Appl Math 39
19.
20.
Lai PL, Fyfe C (2000) Kernel and nonlinear canonical correlation analysis vol. 4: 614–619, cited By (since 1996) 2
21.
Lai P, Fyfe C (2000) Kernel and nonlinear canonical correlation analysis. Int J Neural Syst 10(5):365–377 cited By (since 1996) 75CrossRef
22.
Lykou A, Whittaker J (2010) Sparse cca using a lasso with positivity constraints. Comput Stat Data Anal 54(12):3144–3157 cited By (since 1996) 5MathSciNetCrossRefMATH
23.
Waaijenborg S, Verselewel DeWitt Hamer P, Zwinderman A Quantifying the association between gene expressions and dna-markers by penalized canonical correlation analysis, Stat Appl Genet Mol Biol 7(1), cited By (since 1996) 30
24.
Parkhomenko E, Tritchler D, Beyene J Sparse canonical correlation analysis with application to genomic data integration. Stat Appl Genet Mol Biol 8(1), cited By (since 1996) 32
25.
Vasilescu MAO, Terzopoulos D (2002) Multilinear analysis of image ensembles: tensorfaces. In: ECCV’02: Proceedings of the 7th European conference on computer vision-part I. Springer-Verlag, London, UK, pp. 447–460
26.
Vasilescu MAO, Terzopoulos D (2005) Multilinear independent components analysis. In: Proc. IEEE computer society conference on computer vision and pattern recognition CVPR vol. 1, pp. 547–553. doi:10.​1109/​CVPR.​2005.​240
27.
De Lathauwer L, De Moor B, Vandewalle J (2000) On the best rank-1 and rank-(r1, r2,…, r−n) approximation of higher-order tensors. Siam J Matrix Anal Appl 21(4):1324–1342MathSciNetCrossRefMATH
28.
De Lathauwer L, De Moor B, Vandewalle J (2000) A multilinear singular value decomposition. Siam J Matrix Anal Appl 21(4):1253–1278MathSciNetCrossRefMATH
29.
30.
Ye J (2005) Generalized low rank approximations of matrices. Mach Learn 61(1):167–191CrossRefMATH
31.
He X, Cai D, Niyogi P (2005) Tensor subspace analysis. In: In advances in neural information processing systems 18 (NIPS). MIT Press
32.
Lu HP, Konstantinos NP, Venetsanopoulos AN (2008) MPCA: multilinear principal component analysis of tensor objects. IEEE Trans Neural Netw 19(1):18–39CrossRef
33.
He XF, Niyogi P (2004) Locality preserving projections. Adv Neural Inf Process Sys 16(16):153–160
34.
Wang S-J, Sun M-F, Chen Y-H, Pang E-P, Zhou C-G (2012) STPCA: sparse tensor principal component analysis for feature extraction. In: 21st international conference on pattern recognition, pp. 2278–2281
35.
36.
Hardoon D, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664 cited By (since 1996) 260CrossRefMATH
37.
Branco J, Croux C, Filzmoser P, Oliveira M (2005) Robust canonical correlations: a comparative study. Comput Stat 20(2):203–229 cited By (since 1996) 10MathSciNetCrossRefMATH
38.
Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J Royal Stat Soci Series B Stat Methodol 67(2):301–320 cited By (since 1996) 825MathSciNetCrossRefMATH
39.
Martinez A, Benavente R The AR face database. Univ Purdue, CVC Tech Rep 24
Metadaten
Titel
RETRACTED ARTICLE: Sparse tensor CCA for color face recognition
verfasst von
Shucheng Huang
Jian Chen
Zhi Luo
Publikationsdatum
01.06.2014
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7-8/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-013-1387-x

Weitere Artikel der Ausgabe 7-8/2014

Neural Computing and Applications 7-8/2014 Zur Ausgabe

Original Article

Discrete cuckoo search algorithm for the travelling salesman problem

Original Article

Neural network model for condition monitoring of wear and film thickness in a gearbox

Original Article

Evolutionary techniques versus swarm intelligences: application in reservoir release optimization

Original Article

An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images

Original Article

Strategic developments to improve the optimization performance with efficient optimum solution and produce high wear resistance aluminum–copper alloy matrix composites

Original Article

Homogenous spiking neural P systems with anti-spikes

Premium Partner

    Bildnachweise