Robust shapelets learning: Transform-invariant prototypes

Huiqi Deng, Weifu Chen, Andy J. Ma, Qi Shen, Pong Chi YUEN, Guocan Feng*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

2 Citations (Scopus)


Shapelets are discriminative local patterns in time series, which maximally distinguish among different classes. Instead of considering full series, shapelet transformation considers the existence or absence of local shapelets, which leads to high classification accuracy, easy visualization and interpretability. One of the limitation of existing methods is robustness. For example, Search-based approaches select sample subsequences as shapelets and those methods intuitively may be not accurate and robust enough. Learning-based approaches learn shapelets by maximizing the discriminative ability. However, those methods may not preserve basic shape for visualization. In practice, shapelets are subjected to various geometric transformations, such as translation, scaling, and stretching, which may result in a confusion of shapelet judgement. In this paper, robust shapelet learning is proposed to solve above problems. By learning transform-invariant representative prototypes from all training time series, rather than just selecting samples from the sequences, each time series sample could be approximated by the combination of the transformations of those prototypes. Based on the combination, samples could be easily classified into different classes. Experiments on 16 UCR time series datasets showed that the performance of the proposed framework is comparable to the state-of-art methods, but could learn more representative shapelets for complex scenarios.

Original languageEnglish
Title of host publicationPattern Recognition and Computer Vision - First Chinese Conference, PRCV 2018, Proceedings
EditorsJian-Huang Lai, Cheng-Lin Liu, Tieniu Tan, Xilin Chen, Hongbin Zha, Jie Zhou, Nanning Zheng
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783030033378
Publication statusPublished - 2018
Event1st Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018 - Guangzhou, China
Duration: 23 Nov 201826 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11258 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Representative prototype
  • Robustness
  • Transform-invariant


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