An efficient and effective convolutional auto-encoder extreme learning machine network for 3d feature learning

Neurocomputing

Volumn 174, Issue , 2016, Pages 988-998

  Wang, Yueqing ^a,b   Xie, Zhige ^b   Xu, Kai ^b   Dou, Yong ^a,b   Lei, Yuanwu ^b  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

^b NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Auto encoder; Convolutional; Extreme learning machine; Feature learning

Indexed keywords

CONVOLUTION; CONVOLUTIONAL NEURAL NETWORKS; DATA MINING; DEEP LEARNING; KNOWLEDGE ACQUISITION; NETWORK ARCHITECTURE; NETWORK CODING; THREE DIMENSIONAL COMPUTER GRAPHICS;

3D SHAPE REPRESENTATION; AUTO ENCODERS; CLASSIFICATION ACCURACY; CONVOLUTIONAL; EXTREME LEARNING MACHINE; FEATURE LEARNING; SIGNED DISTANCE FIELDS; STRUCTURAL INFORMATION;

LEARNING SYSTEMS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CONVOLUTIONAL AUTO ENCODER EXTREME LEARNING MACHINE; DATA ANALYSIS; IMAGE ANALYSIS; INFORMATION PROCESSING; INFORMATION PROCESSING DEVICE; INFORMATION RETRIEVAL; MACHINE LEARNING; MATHEMATICAL COMPUTING; MEASUREMENT ACCURACY; PATTERN RECOGNITION; PRIORITY JOURNAL;

EID: 84949650401     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.10.035     Document Type: Article

References (40)

1. Qin F.-w., Li L.-y., Gao S.-m., Yang X.-l., Chen X. A deep learning approach to the classification of 3d cad models. J. Zhejiang University SCIENCE C 2014, 15(2):91-106.
2. Bu S., Han P., Liu Z., Han J., Lin H. Local deep feature learning framework for 3d shape. Comput. Graph. 2015, 46:117-129.
3. Z. Wu, S. Song, A. Khosla, X. Tang, J. Xiao, 3d shapenets for 2.5d object recognition and next-best-view prediction, ArXiv e-prints arXiv:1406.5670 (June 2014). http://www.elsevier.com/xml/linking-roles/preprint.
4. Z. Zhu, X. Wang, S. Bai, C. Yao, X. Bai, Deep learning representation using autoencoder for 3d shape retrieval, CoRR abs/1409.7164.
5. Leng B., Zhang X., Yao M., Xiong Z. A 3d model recognition mechanism based on deep Boltzmann machines. Neurocomputing 151, Part 2 2015, (0):593-602.
6. Y. LeCun, Y. Bengio, Convolutional networks for images, speech, and time series, The handbook of brain theory and neural networks 3361 (10) 1995.
7. Leng B., Guo S., Zhang X., Xiong Z. 3d object retrieval with stacked local convolutional autoencoder. Signal Process. 2015, 112(0):119-128.
8. Z. Zhu, X. Wang, S. Bai, C. Yao, X. Bai, Deep learning representation using autoencoder for 3d shape retrieval, arXiv preprint arXiv:arXiv:1409.7164. http://www.elsevier.com/xml/linking-roles/preprint.
9. Z. Xie, K. Xu, W. Shan, L. Liu, Y. Xiong, H. Huang, Projective feature learning for 3d shapes with multi-view depth images, in: Proceedings of Pacific Graphics, 2015.
10. Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, J. Xiao, 3d shapenets: a deep representation for volumetric shapes, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1912-1920.
11. Huang G.-B., Zhu Q.-Y., Siew C.-K. Extreme learning machine. theory and applications. Neurocomputing 2006, 70(1):489-501.
12. D.-Y. Chen, X.-P. Tian, Y.-T. Shen, M. Ouhyoung, On visual similarity based 3d model retrieval, in: Computer graphics forum, vol. 22, Wiley Online Library, 2003, pp. 223-232.
13. M. Kazhdan, T. Funkhouser, S. Rusinkiewicz, Rotation invariant spherical harmonic representation of 3d shape descriptors, in: Symposium on Geometry Processing, vol. 6, 2003, pp. 156-164.
14. Funkhouser T., Min P., Kazhdan M., Chen J., Halderman A., Dobkin D., Jacobs D. A search engine for 3d models. ACM Trans. Graph. (TOG) 2003, 22(1):83-105.
15. Shapira L., Shalom S., Shamir A., Cohen-Or D., Zhang H. Contextual part analogies in 3D objects. Int. J. Comput. Vis. 2010, 89(2-3):309-326.
16. P. Heider, A. Pierre-Pierre, R. Li, C. Grimm, Local shape descriptors, a survey and evaluation, in: Proceedings of Eurographics Workshop on 3D Object Retrieval, 2011, pp. 49-56.
17. Bronstein A.M., Bronstein M.M., Guibas L.J., Ovsjanikov M. Shape google. geometric words and expressions for invariant shape retrieval. ACM Trans. Graph. (TOG) 2011, 30(1):1.
18. Belongie S., Malik J., Puzicha J. Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24(4):509-522.
19. Chen D.-Y., Tian X.-P., Shen Y.-T., Ouhyoung M. On visual similarity based 3d model retrieval. Comput. Graph. Forum 2003, 22(3):223-232.
20. Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE 86 (11) (1998) 2278-2324.
21. Hinton G.E., Salakhutdinov R.R. Reducing the dimensionality of data with neural networks. Science 2006, 313(5786):504-507.
22. G.E. Hinton, S. Osindero, Y.-W. Teh, A fast learning algorithm for deep belief nets, Neural Comput. 18 (7) (2006) 1527-1554.
23. Kasun L.L.C., Zhou H., Huang G.-B., Vong C.M. Representational learning with elms for big data. IEEE Intell. Syst. 2013, 28(6):31-34.
24. Y. Bengio, A.C. Courville, P. Vincent, Unsupervised feature learning and deep learning: a review and new perspectives, CoRR abs/1206.5538. URL http://arxiv.org/abs/1206.5538.
25. X. Zhang, X. Le, A. Panotopoulou, E. Whiting, C.C. Wang, Perceptual models of preference in 3d printing direction, ACM Trans. Graph. (Proc. SIGGRAPH), 2015.
26. Huang G.-B., Chen L. Convex incremental extreme learning machine. Neurocomputing 2007, 70(16):3056-3062.
27. Huang G.-B., Zhou H., Ding X., Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2012, 42(2):513-529.
28. Rumelhart D.E., Hinton G.E., Williams R.J. Learning representations by back-propagating errors. Cognit. Model. 1988, 5:3.
29. Huang G.-B., Chen L., Siew C.-K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Netw. 2006, 17(4):879-892.
30. Woodbury M.A. Inverting modified matrices. Memo. Rep. 1950, 42:106.
31. Liang N.-Y., Huang G.-B., Saratchandran P., Sundararajan N. A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Netw. 2006, 17(6):1411-1423.
32. Huang G.-B., Chen L. Convex incremental extreme learning machine. Neurocomputing 2007, 70(16):3056-3062.
33. Lin J., Yin J., Cai Z., Liu Q., Li K., Leung V. A secure and practical mechanism of outsourcing extreme learning machine in cloud computing. IEEE Intell. Syst. 2013, 28(6):35-38.
34. He Q., Shang T., Zhuang F., Shi Z. Parallel extreme learning machine for regression based on mapreduce. Neurocomputing 2013, 102:52-58.
35. Wang B., Huang S., Qiu J., Liu Y., Wang G. Parallel online sequential extreme learning machine based on mapreduce. Neurocomputing 2015, 149:224-232.
36. Y. Wang, Y. Dou, X. Liu, Y. Lei, Pr-elm: parallel regularized extreme learning machine based on cluster, Neurocomputing, http://dx.doi.org/10.1016/j.neucom.2015.08.066.
37. J. Tang, C. Deng, G.-B. Huang, Extreme learning machine for multilayer perceptron. IEEE Trans. on Neural Netw. and Learning Syst. 2015, http://dx.doi.org/10.1109/TNNLS.2015.2424995.
38. G.-B. Huang, Zuo Bai, C.M.V. Liyanaarachchi Lekamalage Chamara Kasun, C.L.P. Chen, Local receptive fields based extreme learning machine, IEEE Comput. Intell. Mag. 10 (2), 2015.
39. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst. 2012, 1097-1105.
40. M.D. Zeiler, R. Fergus, Visualizing and understanding convolutional neural networks, arXiv preprint. http://arXiv:1311.2901.