Abstract
Extreme learning machine (ELM) not only is an effective classifier in supervised learning, but also can be applied on unsupervised learning and semi-supervised learning. The model structure of unsupervised extreme learning machine (US-ELM) and semi-supervised extreme learning machine (SS-ELM) are same as ELM, the difference between them is the cost function. We introduce kernel function to US-ELM and propose unsupervised extreme learning machine with kernel (US-KELM). And SS-KELM has been proposed. Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and Wavelet kernel functions have been widely used in support vector machine. Therefore, to realize a combination of the wavelet kernel function, US-ELM, and SS-ELM, unsupervised extreme learning machine with wavelet kernel function (US-WKELM) and semi-supervised extreme learning machine with wavelet kernel function (SS-WKELM) are proposed in this paper. The experimental results show the feasibility and validity of US-WKELM and SS-WKELM in clustering and classification.
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Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501
Cao JW, Lin ZP (2015) Extreme learning machines on high dimensional and large data applications: a survey. Math Probl Eng 2015:1–12
Ding SF, Xu XZ, Nie R (2014) Extreme learning machine and its applications. Neural Comput Appl 25(3):549–556
Liu HP, Sun FC, Yu YL (2014) Multitask extreme learning machine for visual tracking. Cogn Comput 6(3):391–404
Deng WY, Zheng QH, Chen L, Xu XB (2010) Research on extreme learning of neural networks. Chin J Comput 33(2):279–287
Zhang N, Ding SF, Zhang J (2016) Multi layer ELM-RBF for multi-label learning. Appl Soft Comput 43:535–545
Zong WW, Huang GB, Chen YQ (2013) Weighted extreme learning machine for imbalance learning. Neurocomputing 101:229–242
Bai Z, Huang GB, Wang D, Westover MB (2014) Sparse extreme learning machine for classification. IEEE Trans Cybern 44(10):1858–1870
He Q, Jin X, Du CY, Zhuang FZ, Shi ZZ (2014) Clustering in extreme learning machine feature space. Neurocomputing 128:88–95
Huang G, Song SJ, Gupta JND, Wu C (2014) Semi-supervised and unsupervised extreme learning machines. IEEE Trans Cybern 44(12):2405–17
Zheng YH, Jeon B, Xu DH, Jonathan Q, Wu M, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28(2):961–973
Defays D (1977) An efficient algorithm for a complete link method. Comput J 20(4):364–366
Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of 2nd International Conference on KDD. AAAI Press, Menlo Park, pp 226–231
Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416
Tison C, Nicolas JM, Tupin F, Maître H (2004) A new statistical model for Markovian classification of urban areas in high-resolution SAR images. IEEE Trans Geosci Remote Sens 42(10):2046–2057
Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory. ACM Press, New York, pp 92–100
Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434
Liu SL, Feng L, Xiao Y, Wang HB (2014) Robust activation function and its application: semi-supervised kernel extreme learning method. Neurocomputing 144:318–328
Zhang L, Zhou WD, Jiao LC (2004) Wavelet support vector machine. IEEE Trans Syst Man Cybern Part B Cybern 34(1):34–39
Ding SF, Wu FL, Shi ZZ (2014) Wavelet twin support vector machine. Neural Comput Appl 25(6):1241–1247
Filannino M, Liu Z, Cuturi M (2007) UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/
Lee KC, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698
Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: Proceedings of the Second IEEE Workshop on Applications of Computer Vision. IEEE Press, New York, pp 138–142
Papadimitriou CH, Steiglitz K (1998) Combinatorial optimization: algorithms and complexity. Courier Dover Publications, Mineola
Graham DB, Allinson NM (1998) Characterizing virtual eigensignatures for general purpose face recognition. In: Wechsler H, Phillips PJ, Bruce V, Fogelman-Soulie F, Huang TS (eds) Face recognition: from theory to applications. NATO ASI Series F, Computer and Systems Sciences, vol 163, pp 446–456
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This work is supported by the National Natural Science Foundation of China (No.61379101).
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Zhang, N., Ding, S. Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data. Memetic Comp. 9, 129–139 (2017). https://doi.org/10.1007/s12293-016-0198-x
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DOI: https://doi.org/10.1007/s12293-016-0198-x