Maximum Margin Clustering Using Extreme Learning Machine

• Autorzy: Chen Z. , Shixiong X. , Bing L.

Warianty tytułu

PL

Nowa metoda klastrowania – extreme margin clustering EMC w systemach extreme learning machine ELM

• Języki publikacji: EN

Abstrakty

EN

Maximum margin clustering (MMC) is a newly proposed clustering method, which extends large margin computation of support vector machine (SVM) to unsupervised learning. But in nonlinear cases, time complexity is still high. Since extreme learning machine (ELM) has achieved similar generalization performance at much faster learning speed than traditional SVM and LS-SVM, we propose an extreme maximum margin clustering (EMMC) algorithm based on ELM. It can perform well in nonlinear cases. Moreover, the kernel parameters of EMMC need not be tuned by means of random feature mappings. Experimental results on several real-world data sets show that EMMC performs better than traditional MMC methods, especially in handling large scale data sets.

PL

Opisano nową metodę klastrowania „maximum margin clusterung MMC” która rozszerza wielkość marginesu obliczeń numerycznych w systemie SVM z uczeniem bez nadzoru. Nowa metoda EMMC (extreme maximum margin clustering) zapewnia szybsze uczenie, szczególnie w warunkach nieliniowości.

Słowa kluczowe

EN

maximum margin clustering; unsupervised learning; extreme learning machine (ELM); random feature mapping

PL

SVM; klastrowanie; uczenie bez nadzoru

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 1b
• Strony: 202--204
• Opis fizyczny: Bibliogr. 12 poz., wykr.

Twórcy

autor

Chen Z.

• School of Computer Science and Technology, China University of Mining and Technology, Xu Zhou, 221116, China

autor

Shixiong X.

• School of Computer Science and Technology, China University of Mining and Technology, Xu Zhou, 221116, China

autor

Bing L.

• School of Computer Science and Technology, China University of Mining and Technology, Xu Zhou, 221116, China

Bibliografia

• [1] L. Xu, J. Neufeld, B. Larson, D. Schuurmans, Maximum margin clustering, Advances in neural information processing systems, 17 (2004) 1537-1544.
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• [3] K.C. Toh, M.J. Todd, R.H. Tütüncü, SDPT3—a MATLAB software package for semidefinite programming, version 1.3, Optimization methods and software, 11 (1999) 545-581.
• [4] H. Valizadegan, R. Jin, Generalized maximum margin clustering and unsupervised kernel learning, Advances in Neural Information Processing Systems, 19 (2007) 1417.
• [5] K. Zhang, I.W. Tsang, J.T. Kwok, Maximum margin clustering made practical, Neural Networks, IEEE Transactions on, 20 (2009) 583-596.
• [6] G.B. Huang, K. Mao, C.K. Siew, D.S. Huang, Fast modular network implementation for support vector machines, Neural Networks, IEEE Transactions on, 16 (2005) 1651-1663.
• [7] C.W. Hsu, C.J. Lin, A comparison of methods for multiclass support vector machines, Neural Networks, IEEE Transactions on, 13 (2002) 415-425.
• [8] Q. Liu, Q. He, Z. Shi, Extreme support vector machine classifier, Lecture Notes in Computer Science, 5012 (2008) 222-233.
• [9] G.B. Huang, H. Zhou, X. Ding, R. Zhang, Extreme learning machine for regression and multiclass classification, Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, (2010) 1-17.
• [10] Y.H. Pao, G.H. Park, D.J. Sobajic, Learning and generalization characteristics of the random vector functional-link net, Neurocomputing, 6 (1994) 163-180.
• [11] L.P. Wang, C.R. Wan, Comments on “The Extreme Learning Machine”, Neural Networks, IEEE Transactions on, 19 (2008) 1494-1495.
• [12] L. Xu, D. Wilkinson, F. Southey, D. Schuurmans, Discriminative unsupervised learning of structured predictors, in:Poceedings of the 23rd international conference on Machine learning ,2006, pp. 1057-1064.