A large margin time series nearest neighbour classification under locally weighted time warps

Autor: Zhihai Wang, Saeed Varasteh Yazdi, Jidong Yuan, Ahlame Douzal-Chouakria

Publikováno v: Knowledge and Information Systems (KAIS)
Knowledge and Information Systems (KAIS), Springer, 2018, ⟨10.1007/s10115-018-1184-z⟩

Accuracy of the k-nearest neighbour ( $$k\hbox {NN}$$ ) classifier depends strongly on the ability of the used distance to induce k-nearest neighbours of the same class while keeping distant samples of different classes. For time series classificatio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::50fa716925bee13ca07e5dbdcf46850a
https://doi.org/10.1007/s10115-018-1184-z

Time Warp Invariant Dictionary Learning for Time Series Clustering: Application to Music Data Stream Analysis

Autor: Patrick Gallinari, Ahlame Douzal-Chouakria, Saeed Varasteh Yazdi, Manuel Moussallam

Publikováno v: Machine Learning and Knowledge Discovery in Databases ISBN: 9783030109240
ECML/PKDD (1)
ECML/PKDD, 2018
ECML/PKDD, 2018, Sep 2018, Dublin, Ireland

This work proposes a time warp invariant sparse coding and dictionary learning framework for time series clustering, where both input samples and atoms define time series of different lengths that involve variable delays. For that, first an \(l_0\) s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f41d93b9857f91fa2671be11ba00e31
https://doi.org/10.1007/978-3-030-10925-7_22

Time warp invariant k SVD: Sparse coding and dictionary learning for time series under time warp

Autor: Ahlame Douzal-Chouakria, Saeed Varasteh Yazdi

Publikováno v: Pattern Recognition Letters
Pattern Recognition Letters, Elsevier, 2018, 112, pp.1-8. 〈10.1016/j.patrec.2018.05.017〉
Pattern Recognition Letters, Elsevier, 2018, 112, pp.1-8. ⟨10.1016/j.patrec.2018.05.017⟩

Learning dictionary for sparse representing time series is an important issue to extract latent temporal features, reveal salient primitives and sparsely represent complex temporal data. Time series are challenging data, they are often of different d

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61dbb3ba3b8e79ea431c2d5fc6bafebd
https://hal.archives-ouvertes.fr/hal-01811938

Multi-modal and multi-scale temporal metric learning for a robust time series nearest neighbors classification

Autor: Cao-Tri Do, Michèle Rombaut, Saeed Varasteh, Ahlame Douzal-Chouakria, Sylvain Marié

Publikováno v: Information Sciences
Information Sciences, Elsevier, 2017, pp.418-419. 〈10.1016/j.ins.2017.08.020〉
Information Sciences, Elsevier, 2017, 418-419, pp.418-419. ⟨10.1016/j.ins.2017.08.020⟩

International audience; The definition of a metric between time series is inherent to several data analysis and mining tasks, including clustering, classification or forecasting. Time series data present naturally several modalities covering their am

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e70b465e5490b2552e3e7dd905af35d2
https://hal.archives-ouvertes.fr/hal-01579028

Context-Based Persian Multi-document Summarization (Global View).

Autor: Poormasoomi, Asef, Kahani, Mohsen, Yazdi, Saeed Varasteh, Kamyar, Hossein

Publikováno v: 2011 International Conference on Asian Language Processing (IALP); 2011, p145-149, 5p