Top

Neural Computing and Applications

Published in:

31-12-2016 | Original Article

A-optimal convolutional neural network

Authors: Zihong Yin, Dehui Kong, Guoxia Shao, Xinran Ning, Warren Jin, Jing-Yan Wang

Published in: Neural Computing and Applications | Issue 7/2018

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

search-config

AI-assisted search

Off

Abstract

In this paper, we propose a novel data representation-classification model learning algorithm. The model is a convolutional neural network (CNN), and we learn its parameters to achieve A-optimality. The input multi-instance data are represented by a CNN model, and then classified by a linear classification model. The A-optimality of a classification model is measured by the trace of the covariance matrix of the model parameter vector. To achieve the A-optimality of the CNN model, we minimize the classification errors and a regularization term to present the classification model parameter as a function of the CNN filter parameters, and minimize its trace of the covariance matrix. We show that the minimization problem can be solved easily by transferring it to another coupled minimization problem. In the experiments over benchmark data sets of molecular, image, and seismic waveform, we show the advantages of the proposed A-optimal CNN model.

previous article 3D radial invariant of dual Hahn moments

next article Improved Cuckoo Search and Chaotic Flower Pollination optimization algorithm for maximizing area coverage in Wireless Sensor Networks

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

Andrews S, Tsochantaridis I, Hofmann T (2002) Support vector machines for multiple-instance learning. Adv Neural Inf Process Syst 15:561–568

Cai X, Song B (2016) Combining inconsistent textures using convolutional neural networks. J Vis Commun Image Represent 40:366–375CrossRef

Chen Y, Bi J, Wang JZ (2006) Miles: Multiple-instance learning via embedded instance selection. IEEE Trans Pattern Anal Mach Intell 28(12):1931–1947CrossRef

Ciresan DC, Meier U, Gambardella LM, Schmidhube J (2011) Convolutional neural network committees for handwritten character classification. In: 2011 international conference on document analysis and recognition. IEEE, pp 1135–1139

Dietterich TG, Lathrop RH, Lozano-Pérez T (1997) Solving the multiple instance problem with axis-parallel rectangles. Artif Intell 89(1):31–71CrossRef

Ebrahimi M, Suen C, Ormandjieva O (2016) Detecting predatory conversations in social media by deep convolutional neural networks. Digit Investig 18:33–49CrossRef

Fan J, Liang RZ (2016) Stochastic learning of multi-instance dictionary for earth mover’s distance-based histogram comparison. Neural Comput Appl 1–11

Fan X, Malone B, Yuan C (2014) Finding optimal Bayesian network structures with constraints learned from data. In: Proceedings of the 30th conference on uncertainty in artificial intelligence (UAI-2014), pp 200–209

Fan X, Tang K (2010) Enhanced maximum auc linear classifier. In: 2010 seventh international conference on fuzzy systems and knowledge discovery (FSKD), vol 4. IEEE, pp 1540–1544

10.

Fan X, Tang K, Weise T (2011) Margin-based over-sampling method for learning from imbalanced datasets. In: Proceedings of the 15th Pacific-Asia conference on knowledge discovery and data mining (PAKDD-2011). Springer, Berlin, pp 309–320CrossRef

11.

Fan X, Yuan C (2015) An improved lower bound for Bayesian network structure learning. In: Proceedings of the 29th AAAI conference on artificial intelligence (AAAI-2015), pp 3526–3532

12.

Fan X, Yuan C, Malone B (2014) Tightening bounds for Bayesian network structure learning. In: Proceedings of the 28th AAAI conference on artificial intelligence (AAAI-2014), pp 2439–2445

13.

He X, Zhang C, Zhang L, Li X (2016) A-optimal projection for image representation. IEEE Trans Pattern Anal Mach Intell 38(5):1009–1015CrossRef

14.

Janssens O, Slavkovikj V, Vervisch B, Stockman K, Loccufier M, Verstockt S, Van de Walle R, Van Hoecke S (2016) Convolutional neural network based fault detection for rotating machinery. J Sound Vib 377:331–345CrossRef

15.

Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188

16.

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

17.

Leng B, Liu Y, Yu K, Zhang X, Xiong Z (2016) 3d object understanding with 3d convolutional neural networks. Inf Sci 366:188–201MathSciNetCrossRef

18.

Li P, Bu J, Chen C, Wang C, Cai D (2013) Subspace learning via locally constrained a-optimal nonnegative projection. Neurocomputing 115:49–62CrossRef

19.

Li Q, Zhou X, Gu A, Li Z, Liang RZ (2016) Nuclear norm regularized convolutional Max Pos@Top machine. Neural Comput Appl

20.

Li W, Liu H, Yang P, Xie W (2016) Supporting regularized logistic regression privately and efficiently. PloS ONE 11(6):e0156,479CrossRef

21.

Li W, Mo W, Zhang X, Lu Y, Squiers JJ, Sellke EW, Fan W, DiMaio JM, Thatcher JE (2015) Burn injury diagnostic imaging device’s accuracy improved by outlier detection and removal. In: SPIE defense+ security. International Society for Optics and Photonics, pp 947,206–947,206

22.

Li W, Mo W, Zhang X, Squiers JJ, Lu Y, Sellke EW, Fan W, DiMaio JM, Thatcher JE (2015) Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging. J Biomed Opt 20(12):121,305–121,305CrossRef

23.

Liang RZ, Liang G, Li W, Li Q, Wang JJY (2016) Learning convolutional neural network to maximize pos@ top performance measure. arXiv preprint arXiv:1609.08417

24.

Liang RZ, Shi L, Wang H, Meng J, Wang JJY, Sun Q, Gu Y (2016) Optimizing top precision performance measure of content-based image retrieval by learning similarity function. In: 2016 23rd international conference on pattern recognition (ICPR). IEEE

25.

Liang RZ, Xie W, Li W, Wang H, Wang JJY, Taylor L (2016) A novel transfer learning method based on common space mapping and weighted domain matching. In: 2016 IEEE 28th international conference on tools with artificial intelligence (ICTAI)

26.

Lin F, Wang J, Zhang N, Xiahou J, McDonald N (2016) Multi-kernel learning for multivariate performance measures optimization. Neural Comput Appl 1–13

27.

Lin Q, Chen L, Li S, Wu X (2010) A high-resolution fiber optic accelerometer based on intracavity phase-generated carrier (pgc) modulation. Meas Sci Technol 22(1):015,303CrossRef

28.

Lin X, Liu J, Kang X (2016) Audio recapture detection with convolutional neural networks. IEEE Trans Multimedia 18(8):1480–1487CrossRef

29.

Liu H, Yang Z, Wu Z, Li X (2012) A-optimal nonnegative projection for image representation. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1592–1599

30.

Liu X, Wang J, Yin M, Edwards B, Xu P (2015) Supervised learning of sparse context reconstruction coefficients for data representation and classification. Neural Comput Appl 1–9

31.

Matsugu M, Mori K, Mitari Y, Kaneda Y (2003) Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw 16(5):555–559CrossRef

32.

Qiu C, Shen H, Chen L (2015) Towards green cloud computing: demand allocation and pricing policies for cloud service brokerage. In: 2015 IEEE international conference on big data (big data). IEEE, pp 203–212

33.

Sikora M, Wróbel Ł (2010) Application of rule induction algorithms for analysis of data collected by seismic hazard monitoring systems in coal mines. Arch Min Sci 55(1):91–114

34.

Wang H, Wang J (2014) An effective image representation method using kernel classification. In: 2014 IEEE 26th international conference on tools with artificial intelligence (ICTAI 2014), pp 853–858

35.

Wang J, Wang H, Zhou Y, McDonald N (2015) Multiple kernel multivariate performance learning using cutting plane algorithm. In: 2015 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 1870–1875

36.

Wang J, Zhou Y, Duan K, Wang JJY, Bensmail H (2015) Supervised cross-modal factor analysis for multiple modal data classification. In: 2015 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 1882–1888

37.

Wang P, Li W, Gao Z, Zhang J, Tang C, Ogunbona P (2016) Action recognition from depth maps using deep convolutional neural networks. IEEE Trans Hum Mach Syst 46(4):498–509CrossRef

38.

Xie W, Kantarcioglu M, Bush WS, Crawford D, Denny JC, Heatherly R, Malin BA (2014) Securema: protecting participant privacy in genetic association meta-analysis. Bioinformatics 30(23):3334–3341CrossRef

39.

Yang W, Jin L, Tao D, Xie Z, Feng Z (2016) Dropsample: a new training method to enhance deep convolutional neural networks for large-scale unconstrained handwritten chinese character recognition. Pattern Recogn 58:190–203CrossRef

40.

Yang Z, Liu H, Cai D, Wu Z (2016) A-optimal non-negative projection with hessian regularization. Neurocomputing 174:838–849CrossRef

41.

Zhang Y, Daigle BJ, Cohen M, Petzold L (2015) A cure time model for joint prediction of outcome and time-to-outcome. In: 2015 IEEE international conference on data mining (ICDM). IEEE, pp 1117–1122

42.

Zhang Y, Wu TB, Daigle BJ, Cohen M, Petzold L (2016) Identification of disease states associated with coagulopathy in trauma. BMC Med Inform Decis Mak 16(1):124CrossRef

43.

Zhong J, Yang B, Huang G, Zhong F, Chen Z (2016) Remote sensing image fusion with convolutional neural network. Sens Imaging 17(1):10. doi:10.1007/s11220-016-0135-6 CrossRef

Title: A-optimal convolutional neural network
Authors: Zihong Yin
Dehui Kong
Guoxia Shao
Xinran Ning
Warren Jin
Jing-Yan Wang
Publication date: 31-12-2016
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 7/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2783-9

Springer Professional

Abstract

Please log in to get access to your license.

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

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 7/2018

Convolutional neural network-based multimodal image fusion via similarity learning in the shearlet domain

Stability of switched neural networks with time-varying delays

An economic load dispatch and multiple environmental dispatch problem solution with microgrids using interior search algorithm

System performances analysis of reservoir optimization–simulation model in application of artificial bee colony algorithm

An efficient local search for partial vertex cover problem

Engineering strength of fiber-reinforced soil estimated by swarm intelligence optimized regression system

Premium Partner