JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2026, Vol. 61 ›› Issue (4): 84-91.doi: 10.6040/j.issn.1671-9352.0.2024.438

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A depth-based and fusion class information reconstruction method for functional data

HUANG Jiewu, CHEN Xingyue*, WANG Linjie, RAO Wenkang   

  1. School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, Guizhou, China
  • Published:2026-04-08

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Abstract: A depth-based and fusion class information reconstruction method is proposed for partially observed functional data. By applying the depth-based reconstruction method and the inter-class information of sample curves derived from K-means clustering, each partially observed sample curve is reconstructed under different classification scenarios. Then, with the weights dynamically assigned by the self-weighted ensemble learning algorithm, final reconstructed curves are obtained by combining the reconstructed curves of each class. Simulation studies and case analysis show that the proposed method outperforms the depth-based reconstruction method and the regularized regression method under the mean-square prediction error criterion when the proportion of partially observed sample curves in the sample is large. Conversely, the regularized regression method performs better when the proportion of partially observed sample curves is small.

Key words: partially observed functional data, reconstruction of functional data, data depth, class information

CLC Number: 

  • O212.1
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