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一種基于T-分布隨機近鄰嵌入的聚類集成方法

徐森 花小朋 徐靜 徐秀芳 皋軍 安晶

徐森, 花小朋, 徐靜, 徐秀芳, 皋軍, 安晶. 一種基于T-分布隨機近鄰嵌入的聚類集成方法[J]. 電子與信息學報, 2018, 40(6): 1316-1322. doi: 10.11999/JEIT170937
引用本文: 徐森, 花小朋, 徐靜, 徐秀芳, 皋軍, 安晶. 一種基于T-分布隨機近鄰嵌入的聚類集成方法[J]. 電子與信息學報, 2018, 40(6): 1316-1322. doi: 10.11999/JEIT170937
XU Sen, HUA Xiaopeng, XU Jing, XU Xiufang, GAO Jun, AN Jing. Cluster Ensemble Approach Based on T-distributed Stochastic Neighbor Embedding[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1316-1322. doi: 10.11999/JEIT170937
Citation: XU Sen, HUA Xiaopeng, XU Jing, XU Xiufang, GAO Jun, AN Jing. Cluster Ensemble Approach Based on T-distributed Stochastic Neighbor Embedding[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1316-1322. doi: 10.11999/JEIT170937

一種基于T-分布隨機近鄰嵌入的聚類集成方法

doi: 10.11999/JEIT170937
基金項目: 

國家自然科學基金(61105057, 61375001),江蘇省自然科學基金(BK20151299),江蘇省產(chǎn)學研前瞻性聯(lián)合研究項目(BY2016065-01)

Cluster Ensemble Approach Based on T-distributed Stochastic Neighbor Embedding

Funds: 

The National Natural Science Foundation of China (61105057, 61375001), The Natural Science Foundation of Jiangsu Province (BK20151299), The Industry-Education-Research Prospective Project of Jiangsu Province (BY2016065-01)

  • 摘要: 該文將T-分布隨機近鄰嵌入(TSNE)引入到聚類集成問題中,提出一種基于TSNE的聚類集成方法。首先通過TSNE最小化超圖鄰接矩陣的行對應的高維數(shù)據(jù)點與低維映射點分布之間的KL散度,使得高維空間結構在低維空間得以保持,然后在低維空間運行層次聚類算法獲得最終的聚類結果。在基準數(shù)據(jù)集上的實驗結果表明: TSNE能夠提高層次聚類算法的聚類質量,該文方法獲得了優(yōu)于主流聚類集成方法的結果。
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出版歷程
  • 收稿日期:  2017-10-10
  • 修回日期:  2018-03-16
  • 刊出日期:  2018-06-19

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