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 zh_TW
收錄於
會議性質 國內
校內研討會地點 無
研討會時間 20170623~20170624
通訊作者 Pai-Ling Li
國別 TWN
公開徵稿
出版型式
出處 無
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/110479 )

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