Order-preserving sparse coding for sequence classification

Bingbing Ni, Pierre Moulin, Shuicheng Yan

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we investigate order-preserving sparse coding for classifying multi-dimensional sequence data. Such a problem is often tackled by first decomposing the input sequence into individual frames and extracting features, then performing sparse coding or other processing for each frame based feature vector independently, and finally aggregating individual responses to classify the input sequence. However, this heuristic approach ignores the underlying temporal order of the input sequence frames, which in turn results in suboptimal discriminative capability. In this work, we introduce a temporal-order-preserving regularizer which aims to preserve the temporal order of the reconstruction coefficients. An efficient Nesterov-type smooth approximation method is developed for optimization of the new regularization criterion, with guaranteed error bounds. Extensive experiments for time series classification on a synthetic dataset, several machine learning benchmarks, and a challenging real-world RGB-D human activity dataset, show that the proposed coding scheme is discriminative and robust, and it outperforms previous art for sequence classification.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
Number of pages15
EditionPART 2
StatePublished - 2012
Event12th European Conference on Computer Vision, ECCV 2012 - Florence, Italy
Duration: Oct 7 2012Oct 13 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7573 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other12th European Conference on Computer Vision, ECCV 2012

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Order-preserving sparse coding for sequence classification'. Together they form a unique fingerprint.

Cite this