nach oben

Neural Computing and Applications

Erschienen in:

12.11.2020 | Original Article

Flexible data representation with graph convolution for semi-supervised learning

verfasst von: Fadi Dornaika

Erschienen in: Neural Computing and Applications | Ausgabe 12/2021

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

This paper introduces a scheme for semi-supervised data representation. It proposes a flexible nonlinear embedding model that imitates the principle of spectral graph convolutions. Structured data are exploited in order to determine nonlinear and linear models. The introduced scheme takes advantage of data graphs at two different levels. First, it incorporates manifold regularization that is naturally encoded by the graph itself. Second, the regression model is built on the convolved data samples that are obtained by the joint use of the data and their associated graph. The proposed semi-supervised embedding can tackle challenges related to over-fitting in image data spaces. The proposed graph convolution-based semi-supervised embedding paves the way to new theoretical and application perspectives related to the nonlinear embedding. Indeed, building flexible models that adopt convolved data samples can enhance both the data representation and the final performance of the learning system. Several experiments are conducted on six image datasets for comparing the introduced scheme with many state-of-art semi-supervised approaches. These experimental results show the effectiveness of the introduced data representation scheme.

Vorheriger Artikel CNNFOIL: convolutional encoder decoder modeling for pressure fields around airfoils

Nächster Artikel Artificial neural network models for prediction of net radiation over a tropical region

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

http://www.cs.nyu.edu/~roweis/data.html.

www.facepix.org/.

Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434MathSciNetMATH

Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv Neural Inform Process Syst 14:585–591

Cai D, He X, Han J (2007) Semi-supervised discriminant analysis. In: IEEE international conference on computer vision, pp 1–7

Dornaika F, El Traboulsi Y (2016) Learning flexible graph-based semi-supervised embedding. IEEE Trans Cybern 46(1):206–218CrossRef

Dornaika F, Traboulsi YE (2017) Matrix exponential based semi-supervised discriminant embedding. Pattern Recognit 61:92–103CrossRef

Dornaika F, Traboulsi YE (2019) Joint sparse graph and flexible embedding for graph-based semi-supervised learning. Neural Netw 114:91–95CrossRef

Dornaika F, Kejani M, Bosaghzadeh A (2017) Graph construction using adaptive local hybrid coding scheme. Neural Netw 95:91–101CrossRef

El Traboulsi Y, Dornaika F, Assoum A (2015) Kernel flexible manifold embedding for pattern classification. Neurocomputing 167:517–527CrossRef

Fang X, Xu Y, Li X, Lai Z, Wong WK (2015) Learning a nonnegative sparse graph for linear regression

10.

Franceschi L, Niepert M, Pontil M, He X (2019) Learning discrete structures for graph neural networks. In: International conference on machine learning, pp 1972–1982

11.

Hamilton ZYW, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems, pp 1024–1034

12.

Hou C, Nie F, Li X, Yi D, Wu Y (2014) Joint embedding learning and sparse regression: a framework for unsupervised feature selection. IEEE Trans Cybern 44(6):793–804CrossRef

13.

Huang H, Liu J, Pan Y (2012) Semi-supervised marginal fisher analysis for hyperspectral image classification. ISPRS Ann Photogramm Remote Sens Spatial Inform Sci 3:377–382CrossRef

14.

Kar P, Li S, Narasimhan H, Chawla S, Sebastiani F (2016)Online optimization methods for the quantification problem. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining

15.

Kejani MT, Dornaika F, Talebi H (2020) Graph convolution networks with manifold regularization for semi-supervised learning. Neural Netw 127:160–167CrossRef

16.

Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations

17.

Klicpera ABJ, Gunnemann S (2019) Predict then propagate: graph neural networks meet personalized pagerank. In: International conference on learning representations

18.

Korda N, Szorenyi, B, Li S (2016) Distributed clustering of linear bandits in peer to peer networks. In: International conference on machine learning

19.

Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

20.

Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE conference on computer vision and pattern recognition, pp 2169–2178

21.

LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRef

22.

Li LJ, Li FF (2007) What, where and who? Classifying events by scene and object recognition. In: IEEE international conference on computer vision, pp 1–8

23.

Liang Y, You L, Lu X, He Z, Wang H (2018) Low-rank projection learning via graph embedding. Neurocomputing 348:97–106CrossRef

24.

Li S, Chen W, Li S, Leung K-S (2019) Improved algorithm on online clustering of bandits. arXiv:1902.09162

25.

Li S, Karatzoglou A, Gentile C (2016) Collaborative filtering bandits. arXiv:1502.03473

26.

Liu W, Wang J, Chang S-F (2012) Graph-based semi-supervised learning. Proc IEEE 100(9):2624–2638CrossRef

27.

Liu W, Chang S-F (2009) Robust multi-class transductive learning with graphs. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009, pp 381–388. IEEE

28.

Mahadik K, Wu Q, Li S, Sabne A (2020) Fast distributed bandits for online recommendation systems

29.

Nene SA, Nayar S, Murase H (1996) Columbia object image library (COIL-20). In: Technical report CUCS-005-96

30.

Nie F, Xu D, Tsang IW-H, Zhang C (2010) Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction. IEEE Trans Image Process 19(7):1921–1932MathSciNetCrossRef

31.

Nie F, Wang Z, Wang R, Li X (2020) Submanifold-preserving discriminant analysis with an auto-optimized graph. IEEE Trans Cybern 50(8):3682–3695CrossRef

32.

Nie F, Cai G, Li X (2017) Multi-view clustering and semi-supervised classification with adaptive neighbours. In: Thirty-first AAAI conference on artificial intelligence, pp 1501–1511

33.

Nie F, Dong F, Li X (2020) Unsupervised and semisupervised projection with graph optimization. In: IEEE transactions on neural networks and learning systems

34.

Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognit 29(1):51–59CrossRef

35.

Qiao L, Chen S, Tan X (2010) Sparsity preserving discriminant analysis for single training image face recognition. Pattern Recognit Lett 31(5):422–429CrossRef

36.

Qu M, Bengio Y, Tang J (2019) GMNN: graph Markov neural networks. In: International conference on machine learning, pp 5241–5250

37.

Raducanu B, Dornaika F (2012) A supervised non-linear dimensionality reduction approach for manifold learning. Pattern Recognit 45(6):2432–2444CrossRef

38.

Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRef

39.

Tenenbaum JB, De Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323CrossRef

40.

Velickovic WL H P L YB P, Fedus W, Hjelm RD (2019) Deep graph infomax. In: International conference on learning representations

41.

Wan M, Li M, Yang G, Gai S, Jin Z (2014) Feature extraction using two-dimensional maximum embedding difference. Inf Sci 274:55–69CrossRef

42.

Wan M, Lai Z, Yang G, Yang Z, Zhang F, Zheng H (2017) Local graph embedding based on maximum margin criterion via fuzzy set. Fuzzy Sets Syst 318:120–131MathSciNetCrossRef

43.

Wang F, Wang X, Zhang D, Zhang C, Li T (2009) Marginface: a novel face recognition method by average neighborhood margin maximization. Pattern Recognit 42(11):2863–2875CrossRef

44.

Wang H, Leskovec J (2020) Unifying graph convolutional neural networks and label propagation. arXiv preprint arXiv:2002.06755

45.

Wen J, Xu Y, Liu H (2020) Incomplete multiview spectral clustering with adaptive graph learning. IEEE Trans Cybern 50(4):1418–1429CrossRef

46.

Wu H, Prasad S (2018) Semi-supervised dimensionality reduction of hyperspectral imagery using pseudo-labels. Pattern Recognit 74:212–224CrossRef

47.

Wu F, Souza A, Zhang T, Fifty C, Yu T, Weinberger K (2019) Simplifying graph convolutional networks. In: International conference on machine learning, pp 6861–6871

48.

Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51CrossRef

49.

Yu G, Zhang G, Domeniconi C, Yu Z, You J (2012) Semi-supervised classification based on random subspace dimensionality reduction. Pattern Recognit 45(3):1119–1135CrossRef

50.

Yuan Y, Mou L, Lu X (2015) Scene recognition by manifold regularized deep learning architecture. IEEE Trans Neural Netw Learn Syst 26(10):2222–2233MathSciNetCrossRef

51.

Zhang Z, Jia L, Zhao M, Ye Q, Zhang M, Wang M (2018) Adaptive non-negative projective semi-supervised learning for inductive classification. Neural Netw 108:128–145CrossRef

52.

Zhao M, Zhang Y, Zhang Z, Liu J, Kong W (2019) Alg: Adaptive low-rank graph regularization for scalable semi-supervised and unsupervised learning. Neurocomputing 370:16–27CrossRef

53.

Zhu R, Dornaika F, Ruichek Y (2019) Joint graph based embedding and feature weighting for image classification. Pattern Recognit 93:458–469CrossRef

54.

Zhu R, Dornaika F, Ruichek Y (2019) Learning a discriminant graph-based embedding with feature selection for image categorization. Neural Netw 111:35–46CrossRef

55.

Zhu R, Dornaika F, Ruichek Y (2020) Semi-supervised elastic manifold embedding with deep learning architecture. Pattern Recognit 107:107425CrossRef

56.

Zhu X, Ghahramani Z, Lafferty J et al (2003) Semi-supervised learning using Gaussian fields and harmonic functions, vol 3, pp 912–919

Titel: Flexible data representation with graph convolution for semi-supervised learning
verfasst von: Fadi Dornaika
Publikationsdatum: 12.11.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 12/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05462-w

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 12/2021

Random-based networks with dropout for embedded systems

Adaptive RBF neural network-based control of an underactuated control moment gyroscope

An intelligent feature selection approach based on moth flame optimization for medical diagnosis

Design of sparse Bayesian echo state network for time series prediction

Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery

Optimal placement and sizing of FACTS devices for optimal power flow in a wind power integrated electrical network

Premium Partner