Algorithm Research & Explore
|
1765-1768

Segmental feature extraction for functional data

Jin Haibo1
Ma Haiqiang2
1. Dept. of Mathematic, Taiyuan University of Science & Technology, Taiyuan 030024, China
2. School of Statistics, Jiangxi University of Finance & Economics, Nanchang 330013, China

Abstract

Since the representation ability of statistical global feature for functional data classification algorithm is limited, and the salient point feature is susceptible to noise disturbance, this paper proposed a segmental feature extraction algorithm based on statistical depth notion. Firstly, it used the smoothing technique to pre-smooth the discrete observed data, and introduced the first and second derivatives of the functional data. Then, it calculated depths of Mahalanobis integral of the functions and its low-order derivatives in segments, and thus constructed feature vectors of function curves based on the depth measures. Finally, it selected the optimal number of segments for classification by data-driven, and studied the binary classification of function data. Compared with the other curve feature extraction algorithms, experiments on UCR datasets show that the proposed algorithm performs well in extracting the feature of curve, and improves the classification accuracy effectively.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.11.0873
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 6
Section: Algorithm Research & Explore
Pages: 1765-1768
Serial Number: 1001-3695(2020)06-032-1765-04

Publish History

[2020-06-05] Printed Article

Cite This Article

金海波, 马海强. 分段提取函数型数据特征的算法研究 [J]. 计算机应用研究, 2020, 37 (6): 1765-1768. (Jin Haibo, Ma Haiqiang. Segmental feature extraction for functional data [J]. Application Research of Computers, 2020, 37 (6): 1765-1768. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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