research-article

Comparison of Manifold Learning and Deep Learning on Target Classification

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Wang HongmeiAuthors Info & Claims

ICCBDC '17: Proceedings of the 2017 International Conference on Cloud and Big Data Computing

Pages 100 - 104

https://doi.org/10.1145/3141128.3141137

Published: 17 September 2017 Publication History

Abstract

With the development of artificial intelligence, classification tasks become more and more popular, but the amount of data is growing dramatically. There are mainly two ways to deal with this problem, one is to reduce the data dimensions directly, the other one is to take advantage of all data through deep learning. In this paper, we will compare these two data processing methods. The first way is to reduce the extracted features' dimensions through manifold learning and then feed into classifiers, and the other way is to deal it directly with deep learning. The experimental results show that deep learning has a better ability than manifold learning in the classification task.

References

[1]

Permuter H, Francos J, Jermyn I. 2006. A study of Gaussian mixture models of color and texture features for image classification and segmentation. Pattern Recognition,39(4): 695--706.

Digital Library

[2]

Žunić J, Hirota K, Rosin P L. 2010. Hu moment invariant as a shape circularity measure. Pattern Recognition, 43(1): 47--57.

Digital Library

[3]

Banerji S, Sinha A, Liu C. 2013.New image descriptors based on color, texture, shape, and wavelets for object and scene image classification. Neurocomputing, 117: 173--185.

[4]

Ramya K. Raman and C. Chandra Sekhar. 2014. Concept Level Discriminant Analysis Techniques for Dimension Reduction in Image Classification Tasks. In Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing (ICVGIP '14). ACM, New York, NY, USA, Article 44, 8 pages.

Digital Library

[5]

Xiumei Wang, Weifang Liu, Jie Li, and Xinbo Gao. 2016. A novel dimensionality reduction method with discriminative generalized eigen-decomposition. Neurocomput. 173, P2 (January 2016), 163--171.

Digital Library

[6]

Krizhevsky A, Sutskever I, Hinton G E. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 1097--1105.

Digital Library

[7]

Chen Y, Crawford M M, Ghosh J. 2005. Applying nonlinear manifold learning to hyperspectral data for land cover classification. Geoscience and Remote Sensing Symposium, 2005. IGARSS'05. Proceedings. 2005 IEEE International. IEEE, 6: 4311--4314.

[8]

Ma L, Crawford M M, Tian J. 2010.Local manifold learning-based k-nearest-neighbor for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 48(11): 4099--4109.

[9]

Tang B, Song T, Li F, et al. 2014. Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine. Renewable Energy, 62: 1--9.

[10]

LeCun Y, Bottou L, Bengio Y, et al. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11): 2278--2324.

[11]

Ciregan D, Meier U, Schmidhuber J. 2012. Multi-column deep neural networks for image classification. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 3642--3649.

Digital Library

[12]

Johnson J, Karpathy A, Fei-Fei L. 2016. Densecap: Fully convolutional localization networks for dense captioning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4565--4574.

[13]

Simonyan K, Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[14]

Szegedy C, Liu W, Jia Y, et al. 2015. Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition. 1--9.

[15]

Londhe R, Pawar V. 2012. Facial expression recognition based on affine moment invariants. International Journal of Computer Science Issues(IJCSI), 9(6).

[16]

Wang Y, Wang X, Zhang B, et al. 2013. A novel form of affine moment invariants of grayscale images. Elektronika ir Elektrotechnika, 19(1): 77--82.

[17]

Soh L K, Tsatsoulis C. 1999. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Transactions on geoscience and remote sensing, 37(2): 780--795.

[18]

Lowe D G. 1999. Object recognition from local scale-invariant features. Computer vision. The proceedings of the seventh IEEE international conference on. Ieee, 1999, 2: 1150--1157.

Digital Library

[19]

Zhu X, Ma C, Liu B, et al. 2012. Target classification using SIFT sequence scale invariants. Journal of Systems Engineering and Electronics, 23(5): 633--639.

[20]

Schölkopf B, Smola A, Müller K R. 1997. Kernel principal component analysis. International Conference on Artificial Neural Networks. Springer, Berlin, Heidelberg, 583--588.

Digital Library

[21]

Kim K I, Jung K, Kim H J. 2002. Face recognition using kernel principal component analysis. IEEE signal processing letters, 9(2): 40--42.

[22]

Young F W. 2013. Multidimensional scaling: History, theory, and applications. Psychology Press.

[23]

Mao K Z, Tan K C, Ser W. 2000. Probabilistic neural-network structure determination for pattern classification. IEEE Transactions on neural networks, 11(4): 1009--1016.

Digital Library

[24]

Weston J. 2014. Support Vector Machine. Tutorial, http://www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf, accessed, 10(0):0.5.

[25]

Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. Caffe: Convolutional Architecture for Fast Feature Embedding. In Proceedings of the 22nd ACM international conference on Multimedia (MM '14). ACM, New York, NY, USA, 675--678.

Digital Library

Cited By

Saba TRehman AJamail NMarie-Sainte SRaza MSharif M(2021)Categorizing the Students’ Activities for Automated Exam Proctoring Using Proposed Deep L2-GraftNet CNN Network and ASO Based Feature Selection ApproachIEEE Access10.1109/ACCESS.2021.30682239(47639-47656)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3068223

Index Terms

Comparison of Manifold Learning and Deep Learning on Target Classification
1. Applied computing
  1. Arts and humanities
    1. Architecture (buildings)
      1. Computer-aided design

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ICCBDC '17: Proceedings of the 2017 International Conference on Cloud and Big Data Computing

September 2017

135 pages

ISBN:9781450353434

DOI:10.1145/3141128

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Northumbria University: University of Northumbria at Newcastle

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 September 2017

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Research-article
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Refereed limited

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National Natural Science Foundation of China
Aeronautical Science Foundation of China
Key Research and Development Program of Shaanxi Province

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ICCBDC 2017

ICCBDC 2017: 2017 International Conference on Cloud and Big Data Computing

September 17 - 19, 2017

London, United Kingdom

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Cited By

Saba TRehman AJamail NMarie-Sainte SRaza MSharif M(2021)Categorizing the Students’ Activities for Automated Exam Proctoring Using Proposed Deep L2-GraftNet CNN Network and ASO Based Feature Selection ApproachIEEE Access10.1109/ACCESS.2021.30682239(47639-47656)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3068223

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