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Issue No.06 - Nov.-Dec. (2013 vol.28)
pp: 30-59
Erik Cambria , MIT Media Laboratory
Guang-Bin Huang , Nanyang Technological University, Singapore
Liyanaarachchi Lekamalage Chamara Kasun , School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Hongming Zhou , School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Chi Man Vong , Faculty of Science and Technology, University of Macau
Jiarun Lin , National University of Defense Technology, China
Jianping Yin , National University of Defense Technology, China
Zhiping Cai , National University of Defense Technology, China
Qiang Liu , National University of Defense Technology, China
Kuan Li , National University of Defense Technology, China
Victor C.M. Leung , University of British Columbia, Vancouver, Canada
Liang Feng , School of Computer Engineering, Nanyang Technological University, Singapore
Yew-Soon Ong , School of Computer Engineering, Nanyang Technological University, Singapore
Meng-Hiot Lim , School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Anton Akusok , Aalto University, Finland
Amaury Lendasse , Aalto University, Finland
Francesco Corona , Aalto University, Finland
Rui Nian , Ocean University, China
Yoan Miche , Aalto University, Finland
Paolo Gastaldo , University of Genoa, Italy
Rodolfo Zunino , University of Genoa, Italy
Sergio Decherchi , Italian Institute of Technology, Italy
Xuefeng Yang , School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Kezhi Mao , School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Beom-Seok Oh , School of Electrical and Electronics Engineering, Yonsei University, Korea
Jehyoung Jeon , School of Electrical and Electronics Engineering, Yonsei University, Korea
Kar-Ann Toh , School of Electrical and Electronics Engineering, Yonsei University, Korea
Andrew Beng Jin Teoh , School of Electrical and Electronics Engineering, Yonsei University, Korea
Jaihie Kim , School of Electrical and Electronics Engineering, Yonsei University, Korea
Hanchao Yu , Institute of Computing, Chinese Academy of Sciences, China
Yiqiang Chen , Institute of Computing, Chinese Academy of Sciences, China
Junfa Liu , Institute of Computing, Chinese Academy of Sciences, China
ABSTRACT
This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation. In "Representational Learning with ELMs for Big Data," Liyanaarachchi Lekamalage Chamara Kasun, Hongming Zhou, Guang-Bin Huang, and Chi Man Vong propose using the ELM as an auto-encoder for learning feature representations using singular values. In "A Secure and Practical Mechanism for Outsourcing ELMs in Cloud Computing," Jiarun Lin, Jianping Yin, Zhiping Cai, Qiang Liu, Kuan Li, and Victor C.M. Leung propose a method for handling large data applications by outsourcing to the cloud that would dramatically reduce ELM training time. In "ELM-Guided Memetic Computation for Vehicle Routing," Liang Feng, Yew-Soon Ong, and Meng-Hiot Lim consider the ELM as an engine for automating the encapsulation of knowledge memes from past problem-solving experiences. In "ELMVIS: A Nonlinear Visualization Technique Using Random Permutations and ELMs," Anton Akusok, Amaury Lendasse, Rui Nian, and Yoan Miche propose an ELM method for data visualization based on random permutations to map original data and their corresponding visualization points. In "Combining ELMs with Random Projections," Paolo Gastaldo, Rodolfo Zunino, Erik Cambria, and Sergio Decherchi analyze the relationships between ELM feature-mapping schemas and the paradigm of random projections. In "Reduced ELMs for Causal Relation Extraction from Unstructured Text," Xuefeng Yang and Kezhi Mao propose combining ELMs with neuron selection to optimize the neural network architecture and improve the ELM ensemble's computational efficiency. In "A System for Signature Verification Based on Horizontal and Vertical Components in Hand Gestures," Beom-Seok Oh, Jehyoung Jeon, Kar-Ann Toh, Andrew Beng Jin Teoh, and Jaihie Kim propose a novel paradigm for hand signature biometry for touchless applications without the need for handheld devices. Finally, in "An Adaptive and Iterative Online Sequential ELM-Based Multi-Degree-of-Freedom Gesture Recognition System," Hanchao Yu, Yiqiang Chen, Junfa Liu, and Guang-Bin Huang propose an online sequential ELM-based efficient gesture recognition algorithm for touchless human-machine interaction.
INDEX TERMS
Special issues and sections, Learning systems, Nonhomogeneous media, Data visualization, Biological neural networks, Big data, Adaptive learning, Machine learning, Artificial intelligence, Gesture recognition,random projections, extreme learning machine, ELM, cloud computing, computation outsourcing, partitioned ELM, knowledge extraction, text mining, ensemble, signature biometrics, hand gesture signature verification, Kinect, evolutionary optimization, memetic computation, meta meme, deep networks, representational learning, visualization, random permutations, online sequential ELM, OS-ELM, human-computer interaction
CITATION
Erik Cambria, Guang-Bin Huang, Liyanaarachchi Lekamalage Chamara Kasun, Hongming Zhou, Chi Man Vong, Jiarun Lin, Jianping Yin, Zhiping Cai, Qiang Liu, Kuan Li, Victor C.M. Leung, Liang Feng, Yew-Soon Ong, Meng-Hiot Lim, Anton Akusok, Amaury Lendasse, Francesco Corona, Rui Nian, Yoan Miche, Paolo Gastaldo, Rodolfo Zunino, Sergio Decherchi, Xuefeng Yang, Kezhi Mao, Beom-Seok Oh, Jehyoung Jeon, Kar-Ann Toh, Andrew Beng Jin Teoh, Jaihie Kim, Hanchao Yu, Yiqiang Chen, Junfa Liu, "Extreme Learning Machines [Trends & Controversies]", IEEE Intelligent Systems, vol.28, no. 6, pp. 30-59, Nov.-Dec. 2013, doi:10.1109/MIS.2013.140
REFERENCES
1. G.-B. Huang,L. Chen,, and C.-K. Siew,“Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes,” IEEE Trans. Neural Networks, vol. 17, no. 4, 2006, pp. 879-892.
2. G.-B. Huang,X. Ding,, and H. Zhou,“Optimization Method Based Extreme Learning Machine for Classification,” Neurocomputing, vol. 74, 2010, pp. 155-163.
3. G.-B. Huang,Q.-Y. Zhu,, and C.-K. Siew,“Extreme Learning Machine: Theory and Applications,” Neuro computing, vol. 70, 2006, pp. 489-501.
4. G.-B. Huang et al., “Extreme Learning Machine for Regression and Multiclass Classification,” IEEE Trans. Systems, Man, and Cybernetics, vol. 42, no. 2, 2011, pp. 513-529.
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