Convolutional nonlinear neighbourhood components analysis for time series classification

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

6 Scopus Citations
View graph of relations

Author(s)

  • Yi Zheng
  • Qi Liu
  • Enhong Chen
  • Liang He
  • Guangyi Lv

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication19th Pacific-Asia Conference, PAKDD 2015, Proceedings
EditorsTru Cao, Ee-Peng Lim, Tu-Bao Ho, Zhi-Hua Zhou, Hiroshi Motoda, David Cheung
PublisherSpringer Verlag
Pages534-546
Volume9078
ISBN (Print)9783319180311
StatePublished - 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9078
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015
PlaceViet Nam
CityHo Chi Minh City
Period19 - 22 May 2015

Abstract

During last decade, tremendous efforts have been devoted to the research of time series classification. Indeed, many previous works suggested that the simple nearest-neighbor classification is effective and difficult to beat. However, we usually need to determine the distance metric (e.g., Euclidean distance and Dynamic Time Warping) for different domains, and current evidence shows that there is no distance metric that is best for all time series data. Thus, the choice of distance metric has to be done empirically, which is time expensive and not always effective. To automatically determine the distance metric, in this paper, we investigate the distance metric learning and propose a novel Convolutional Nonlinear Neighbourhood Components Analysis model for time series classification. Specifically, our model performs supervised learning to project original time series into a transformed space. When classifying, nearest neighbor classifier is then performed in this transformed space. Finally, comprehensive experimental results demonstrate that our model can improve the classification accuracy to some extent, which indicates that it can learn a good distance metric.

Citation Format(s)

Convolutional nonlinear neighbourhood components analysis for time series classification. / Zheng, Yi; Liu, Qi; Chen, Enhong; Zhao, J. Leon; He, Liang; Lv, Guangyi.

Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015, Proceedings. ed. / Tru Cao; Ee-Peng Lim; Tu-Bao Ho; Zhi-Hua Zhou; Hiroshi Motoda; David Cheung. Vol. 9078 Springer Verlag, 2015. p. 534-546 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9078).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review