Clustering for multivariate functional data
學年 105
學期 1
發表日期 2016-08-23
作品名稱 Clustering for multivariate functional data
作品名稱（其他語言）
著者 Li, Pai-Ling; Kuo, Ling-Cheng
作品所屬單位
出版者
會議名稱 The 22nd International Conference on Computational Statistics
會議地點 Oviedo, Spain
摘要 A novel multivariate k-centers functional clustering algorithm for the multivariate functional data is proposed. We assume that clusters can be defined via the 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 each variable represents the importance of a variable to the cluster information and is determined by the within-variable variation or the between-variable correlations. The proposed method can take the means and modes of variation differentials among groups of each variable into account simultaneously. In addition, the weight of the proposed algorithm is flexible and can be chosen by the objective of clustering. Numerical performance of the proposed method is examined by simulation studies, with an application to a data example.
關鍵字
語言 en
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20160823~20160826
通訊作者
國別 ESP
公開徵稿
出版型式
出處 Book of Abstracts of COMPSTAT 2016
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107583 )

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