UM
Broad learning system: Feature extraction based on K-means clustering algorithm
Liu Z.2; Zhou J.1; Chen C.L.P.2
2017-10-31
Source PublicationICCSS 2017 - 2017 International Conference on Information, Cybernetics, and Computational Social Systems
Pages683-687
AbstractBroad Learning System [1] proposed recently demonstrates efficient and effective learning capability. This model is also proved to be suitable for incremental learning algorithms by taking the advantages of random vector flat neural networks. In this paper, a modified BLS structure based on the K-means feature extraction is developed. Compared with the original broad learning system, acceptable performance on more complicated data set, such as CIFAR-10, is achieved. Furthermore, it is proved that the proposed model in [1] is flexible and potential in various applications.
Keywordbroad learning system deep learning feature representation incremental learning K-means random vector functional link networks Single layer feedforward neural networks SVD
DOI10.1109/ICCSS.2017.8091501
URLView the original
Language英語
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.University of Jinan
2.Universidade de Macau
3.Institute of Automation Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Liu Z.,Zhou J.,Chen C.L.P.. Broad learning system: Feature extraction based on K-means clustering algorithm[C],2017:683-687.
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