SNF效應的理論解釋和高影響力聚類特徵的識別;Theoretical Explanation of the SNF Effects and Identification of High-Impact Clustering Features

NCU Institutional Repository > 理學院 > 統計研究所 > 博碩士論文 > Item 987654321/95268

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/95268

题名:	SNF效應的理論解釋和高影響力聚類特徵的識別;Theoretical Explanation of the SNF Effects and Identification of High-Impact Clustering Features
作者:	陸彥廷;Lu, Yen-Ting
贡献者:	統計研究所
关键词:	聚類;融合;高維度資訊準則;多元邏輯迴歸;clustering;fusion;high-dimensional information criterion;multinomial logistic regression
日期:	2024-07-10
上传时间:	2024-10-09 16:36:50 (UTC+8)
出版者:	國立中央大學
摘要:	本研究從兩個角度討論高維度資料聚類。首先，我們從理論上解釋了相似網絡融合（SNF）對聚類的影響。人們發現，SNF 透過融合從不同特徵集計算出的相似性矩陣，可以增強許多應用中的聚類性能。所提出的理論解釋有助於更深入地理解融合的功能及其限制。接下來，我們提出了一個由多元邏輯迴歸和高維度資訊標準組成的迭代過程（以 SF-MLR 表示），以從大量特徵集中識別高影響力的聚類特徵。我們將SF-MLR 應用於幾個高維度資料集。數值結果表明，SF-MLR 可以識別高影響力的聚類特徵，這也有助於提高聚類性能。;This study discusses high-dimensional data clustering from two perspectives. First, we provide a theoretical explanation of the effect of similarity network fusion (SNF) on clustering. The SNF has been found to enhance clustering performances in many applications by fusing the similarity matrices computed from different feature sets. The proposed theoretical explanation helps a deeper understanding of how the fusion functions and where its limitations are. Next, we propose an iterative procedure consisting of multinomial logistic regression and high-dimensional information criterion, denoted by SF-MLR, to identify high-impact clustering features from a vast feature set. We apply the SF-MLR to several high-dimensional datasets. The numerical results reveal that the SF-MLR can identify high-impact clustering features, which also helps to improve clustering performances.
显示于类别:	[統計研究所] 博碩士論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	55	检视/开启

在NCUIR中所有的数据项都受到原著作权保护.

社群 sharing

数据加载中.....