Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-33709-3_13
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dc.titleOrder-preserving sparse coding for sequence classification
dc.contributor.authorNi, B.
dc.contributor.authorMoulin, P.
dc.contributor.authorYan, S.
dc.date.accessioned2014-06-19T03:22:33Z
dc.date.available2014-06-19T03:22:33Z
dc.date.issued2012
dc.identifier.citationNi, B.,Moulin, P.,Yan, S. (2012). Order-preserving sparse coding for sequence classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7573 LNCS (PART 2) : 173-187. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-33709-3_13" target="_blank">https://doi.org/10.1007/978-3-642-33709-3_13</a>
dc.identifier.isbn9783642337086
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/71335
dc.description.abstractIn 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. © 2012 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-33709-3_13
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/978-3-642-33709-3_13
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume7573 LNCS
dc.description.issuePART 2
dc.description.page173-187
dc.identifier.isiutNOT_IN_WOS
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