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
Neural Computing and Applications
Published in: Neural Computing and Applications 11/2019

21-06-2018 | Original

A deep discriminative and robust nonnegative matrix factorization network method with soft label constraint

Authors: Ming Tong, Yiran Chen, Mengao Zhao, Haili Bu, Shengnan Xi

Published in: Neural Computing and Applications | Issue 11/2019

Log in

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

search-config
loading …

Abstract

In order to obtain a discriminative, compact and robust data representation, a discriminative and robust nonnegative matrix factorization method with soft label constraint (DRNMF_SLC) is proposed. By minimizing the objective function, the data representation after learning soft label constraint is obtained. To further acquire a more hierarchical and discriminative data representation, a deep discriminative and robust nonnegative matrix factorization network method with soft label constraint (Deep DRNMFN_SLC) is constructed. In order to improve the feature expression ability of deep neural network (DNN), a deep discriminative and robust nonnegative matrix factorization network method with soft label constraint based on DNN (Deep DRNMFN_SLC_DNN) is proposed, which could obtain a more discriminative, robust and generalized feature representation, and meanwhile greatly reduce the dimension of data features. Furthermore, the objective function of DRNMF_SLC is constructed by introducing both the global loss function and the central loss function of soft label constraint matrix, and the optimization solution and convergence proof of objective function are given simultaneously. When the proposed DRNMF_SLC method and Deep DRNMFN_SLC_DNN method are, respectively, applied to the face recognition under occlusions and illumination variations, the frameworks, Algorithm 1 and Algorithm 2 are given. The extensive and adequate experiments demonstrate the effectiveness of the proposed method.
previous article Artificial intelligence design charts for predicting friction capacity of driven pile in clay
next article NFC-ARP: neuro-fuzzy controller for adaptive resource provisioning in virtualized environments

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

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+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
Appendix
Available only for authorised users
Literature
1.
Sun Y, Mao H, Sang Y et al (2017) Explicit guiding auto-encoders for learning meaningful representation. Neural Comput Appl 28(3):429–436CrossRef
2.
Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Int 19(7):711–720CrossRef
3.
He J, Bi Y, Ding L et al (2017) Unsupervised feature selection based on decision graph. Neural Comput Appl 28(10):3047–3059CrossRef
4.
Li Z, Liu J, Yang Y et al (2014) Clustering-guided sparse structural learning for unsupervised feature selection. IEEE Trans Know Data Eng 26(9):2138–2150CrossRef
5.
Yan H, Yang J (2015) Sparse discriminative feature selection. Pattern Recognit 48(5):1827–1835CrossRef
6.
Li Z, Liu J, Tang J et al (2015) Robust structured subspace learning for data representation. IEEE Trans Pattern Anal Mach Int 37(10):2085–2098CrossRef
7.
Feng Y, Xiao J, Zhou K et al (2015) A locally weighted sparse graph regularized non-negative matrix factorization method. Neurocomputing 169:68–76CrossRef
8.
Pang Y, Wang S, Yuan Y (2014) Learning regularized LDA by clustering. IEEE Trans Neural Netw Learn Syst 25(12):2191–2201CrossRef
9.
He X, Niyogi P (2004) Locality preserving projections. In: Advances in neural information processing systems pp 153–160
10.
Zhang H, Wu QMJ, Chow TWS et al (2012) A two-dimensional neighborhood preserving projection for appearance-based face recognition. Pattern Recognit 45(5):1866–1876CrossRef
11.
Yan S, Xu D, Zhang B et al (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Int 29(1):40–51CrossRef
12.
Tenenbaum JB, Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323CrossRef
13.
Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396CrossRef
14.
Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRef
15.
Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791CrossRef
16.
Li SZ, Hou XW, Zhang HJ et al (2001) Learning spatially localized, parts-based representation. In: IEEE conference on computer vision and pattern recognition, pp 207–212
17.
Pascual-Montano A, Carazo JM, Kochi K et al (2006) Nonsmooth nonnegative matrix factorization (nsNMF). IEEE Trans Pattern Anal Mach Int 28(3):403–415CrossRef
18.
Cai D, He X, Han J et al (2011) Graph regularized nonnegative matrix factorization for data representation. IEEE Trans Pattern Anal Mach Int 33(8):1548–1560CrossRef
19.
Wang Y, Jia Y, Hu C et al (2004) Fisher non-negative matrix factorization for learning local features. In: Proceedings of Asian conference on computer vision, pp 27–30
20.
Zafeiriou S, Tefas A, Buciu I et al (2006) Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification. Neural Netw 17(3):683–695CrossRef
21.
Liu H, Wu Z, Li X et al (2012) Constrained nonnegative matrix factorization for image representation. IEEE Trans Pattern Anal Mach Int 34(7):1299–1311CrossRef
22.
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: ImageNet challenge, pp 1–10
23.
Schroff F, Kalenichenko D, Philbin J (2015) FaceNet: a unified embedding for face recognition and clustering. In: IEEE conference on computer vision and pattern recognition, pp 815–823
24.
Sun Y, Chen Y, Wang X et al (2014) Deep learning face representation by joint identification-verification. In: Advances in neural information processing systems, pp 1988–1996
25.
Sun Y, Wang X, Tang X (2015) Deeply learned face representations are sparse, selective, and robust. In: IEEE conference on computer vision and pattern recognition, pp 2892–2900
26.
27.
Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions. In: IEEE conference on computer vision and pattern recognition, pp 1–9
28.
Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition. Proc Br Mach Vis Conf 1(3):6
29.
30.
Zhang H, Cao X, Ho JKL et al (2017) Object-level video advertising: an optimization framework. IEEE Trans Ind Inf 13(2):520–531CrossRef
31.
Mehdipour Ghazi M, Kemal Ekenel H (2016) A comprehensive analysis of deep learning based representation for face recognition. In: IEEE conference on computer vision and pattern recognition, pp 34–41
32.
Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958MathSciNetMATH
33.
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105
34.
Zheng W (2014) Multi-view facial expression recognition based on group sparse reduced-rank regression. IEEE Trans Affect Comput 5(1):71–85CrossRef
35.
Li J, Zhao J, Zhao F et al (2016) Robust face recognition with deep multi-view representation learning. In: Proceedings of the 2016 ACM on multimedia conference, pp 1068–1072
36.
Wu F, Jing XY, You X et al (2016) Multi-view low-rank dictionary learning for image classification. Pattern Recognit 50:143–154CrossRef
37.
Song HA, Kim BK, Xuan TL et al (2015) Hierarchical feature extraction by multi-layer non-negative matrix factorization network for classification task. Neurocomputing 165:63–74CrossRef
38.
Huang GB, Lee H, Learned-Miller E (2012) Learning hierarchical representations for face verification with convolutional deep belief networks. In: IEEE conference on computer vision and pattern recognition, pp 2518–2525
39.
Trigeorgis G, Bousmalis K, Zafeiriou S et al (2017) A deep matrix factorization method for learning attribute representations. IEEE Trans Pattern Anal Mach Int 39(3):417–429CrossRef
40.
Ouyang W, Wang X (2012) A discriminative deep model for pedestrian detection with occlusion handling. In: IEEE conference on computer vision and pattern recognition, pp 3258–3265
41.
Chan TH, Jia K, Gao S et al (2015) PCANet: a simple deep learning baseline for image classification? IEEE Trans Image Process 24(12):5017–5032MathSciNetCrossRef
42.
Zhen L, Yi D, Li SZ (2016) Learning stacked image descriptor for face recognition. IEEE Trans Circuits Syst Video Technol 26(9):1685–1696CrossRef
43.
Hosseini-Asl E, Zurada JM, Nasraoui O (2016) Deep learning of part-based representation of data using sparse autoencoders with nonnegativity constraints. IEEE Trans Neural Netw Learn Syst 27(12):2486–2498CrossRef
44.
Babenko A, Slesarev1 A, Chigorin A et al (2014) Neural codes for image retrieval. In: Proceedings of European conference on computer vision, pp 584–599
45.
Yang S, Luo P, Loy CC et al (2015) Deep representation learning with target coding. In: AAAI, pp 3848–3854
46.
Cao Y, Long M, Wang J et al (2016) Deep quantization network for efficient image retrieval. In: AAAI, pp 3457–3463
47.
Gui L, Morency LP (2015) Learning and transferring deep ConvNet representations with group-sparse factorization. In: International conference on computer vision
48.
Martinez AR, Benavente R (1998) The AR face database. CVC technical report 24, Barcelona, Spain
49.
Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: Proceedings of the second IEEE workshop on applications of computer vision, pp 138–142
50.
Sim T, Baker S, Bsat M (2002) The CMU pose, illumination, and expression (PIE) database. In: Fifth IEEE international conference on automatic face and gesture recognition
51.
Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Int 23(6):643–660CrossRef
52.
Zhang R, Hu Z, Pan G et al (2016) Robust discriminative non-negative matrix factorization. Neurocomputing 173:552–561CrossRef
53.
Jia Y, Shelhamer E, Donahue J et al (2014) Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on multimedia, pp 675–678
54.
Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605MATH
Metadata
Title
A deep discriminative and robust nonnegative matrix factorization network method with soft label constraint
Authors
Ming Tong
Yiran Chen
Mengao Zhao
Haili Bu
Shengnan Xi
Publication date
21-06-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 11/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3554-6

Other articles of this Issue 11/2019

Neural Computing and Applications 11/2019 Go to the issue

Original Article

A discriminant graph nonnegative matrix factorization approach to computer vision

Original Article

Assessment of bearing capacity for strip footing located near sloping surface considering ANN model

Original Article

A hybridized ELM using self-adaptive multi-population-based Jaya algorithm for currency exchange prediction: an empirical assessment

Original Article

Modeling a robust wind-speed forecasting to apply to wind-energy production

Original Article

Gegenbauer wavelets collocation-based scheme to explore the solution of free bio-convection of nanofluid in 3D nearby stagnation point

Original Article

A subjectivity classification framework for sports articles using improved cortical algorithms

Premium Partner

    Image Credits