Temporal extension of Laplacian Eigenmaps for unsupervised dimensionality reduction of time series

M. Lewandowski, J. Martinez-del-Rincon, D. Makris, J.-C. Nebel

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

36 Citations (Scopus)

Abstract

A novel non-linear dimensionality reduction method, called Temporal Laplacian Eigenmaps, is introduced to process efficiently time series data. In this embedded-based approach, temporal information is intrinsic to the objective function, which produces description of low dimensional spaces with time coherence between data points. Since the proposed scheme also includes bidirectional mapping between data and embedded spaces and automatic tuning of key parameters, it offers the same benefits as mapping-based approaches. Experiments on a couple of computer vision applications demonstrate the superiority of the new approach to other dimensionality reduction method in term of accuracy. Moreover, its lower computational cost and generalisation abilities suggest it is scalable to larger datasets.
Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
Pages161-164
Number of pages4
DOIs
Publication statusPublished - 01 Jan 2010

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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