教師資料查詢 | 類別: 會議論文 | 教師: 李百靈 Pai-ling Li (瀏覽個人網頁)

標題:Clustering for Multivariate Functional Data
學年105
學期2
發表日期2017/06/23
作品名稱Clustering for Multivariate Functional Data
作品名稱(其他語言)
著者Pai-Ling Li; Ling-Cheng Kuo
作品所屬單位
出版者
會議名稱第二十六屆南區統計研討會
會議地點國立臺北大學三峽校區商學大樓
摘要We propose a multivariate k-centers functional clustering algorithm for the multivariate functional data. We assume that clusters can be defined via functional principal components subspace projection for each variable. A newly observed subject with multivariate functions is classified into a best-predicted cluster by minimizing a weighted distance measure, which is a weighted sum of discrepancies in observed functions and their corresponding projections onto the subspaces for all variables, among all the clusters. The weight of the proposed algorithm is flexible and can be chosen by the objective of clustering. The proposed method can take the means and modes of variation differentials among groups of each variable into account simultaneously. Numerical performance of the proposed method is demonstrated by simulation studies, with an application to a data example.
關鍵字cluster analysis;functional principal components analysis;multivariate functional data
語言中文
收錄於
會議性質國內
校內研討會地點
研討會時間20170623~20170624
通訊作者Pai-Ling Li
國別中華民國
公開徵稿
出版型式
出處
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