UM
Multiple kernel shadowed clustering in approximated feature space
Zhao, Yin-Ping; Chen, Long; Chen, C. L. Philip
2018
Conference Name3rd International Conference on Data Mining and Big Data, DMBD 2018 held in conjunction with the 9th International Conference on Swarm Intelligence, ICSI 2018
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10943 LNCS
Pages265-275
Conference Date6 17, 2018 - 6 22, 2018
Conference PlaceShanghai, China
Author of SourceSpringer Verlag
AbstractCompared to conventional fuzzy clustering, shadowed clustering possesses several advantages, such as better modeling of the uncertainty for the overlapped data, reduction of computation and more robust to outliers because of the generated shadowed partitions. Based on the construction of a set of pre-specific kernels, multiple kernel fuzzy clustering presents more flexibility in fuzzy clustering than kernel fuzzy clustering. However, it is unattainable to large dataset because of its high computational complexity. To solve this problem, a new multiple kernel shadowed clustering in approximated feature space is proposed herein, using Random Fourier Features and Spherical Random Fourier Features to approximate radial basis kernels and polynomial kernels, respectively. To optimize the kernel weight, maximum-entropy regularization is used. The results of our proposed algorithm on Iris and Letter Recognition datasets show better performance than other algorithms in comparison. © Springer International Publishing AG, part of Springer Nature 2018.
DOI10.1007/978-3-319-93803-5_25
Language英语
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Citation statistics
Document TypeConference paper
CollectionUniversity of Macau
AffiliationUniversity of Macau, Taipa, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zhao, Yin-Ping,Chen, Long,Chen, C. L. Philip. Multiple kernel shadowed clustering in approximated feature space[C]//Springer Verlag,2018:265-275.
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