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Hyper-parameter Analysis of the Improved Deep Embedding Clustering Model
Feng,Qiying1; Chen,C. L.Philip1; Zhou,Jin2
2018-12-01
Source Publication2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018
Pages57-60
AbstractClustering has gained large successful in many areas in machine learning especially the computer vision and knowledge discovery. However, the clustering tasks on the high-dimension data is still the bottleneck of the clustering. And this problem mainly due to the lack of representative and important features of the data. Recently, the efficient deep clustering models including the deep embedded clustering model (DEC) and the improved deep embedded clustering model (IDEC) are proposed to solve this problem. They validated their models on the real-world datasets and improved the clustering performance compared with the traditional clustering algorithm, which have demonstrated that the superior of DEC and IDEC. Both the clustering method used in DEC and IDEC are student-t distribution, and we choose the IDEC model for further studying the robustness of these two models. Therefore, we discuss how sensitivity of hyper-parameter influence in the IDEC model, and comparative experiments are given to show the sensitivities of the hyper-meters in IDEC in term of the clustering performances.
KeywordAuto-encoder Deep Learning high-dimension real-world clustering student-t distribution
DOI10.1109/SPAC46244.2018.8965509
URLView the original
Language英語English
Scopus ID2-s2.0-85079170324
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.University of Macau,Faculty of Science and Technology,Macao
2.University of Jinan,China
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Feng,Qiying,Chen,C. L.Philip,Zhou,Jin. Hyper-parameter Analysis of the Improved Deep Embedding Clustering Model[C], 2018, 57-60.
APA Feng,Qiying., Chen,C. L.Philip., & Zhou,Jin (2018). Hyper-parameter Analysis of the Improved Deep Embedding Clustering Model. 2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018, 57-60.
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