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 |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Machine Learning and Information Access (MLIA), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2019 |
| Předmět: |
sparse coding
Computer science Time series clustering 020206 networking & telecommunications 02 engineering and technology Matching pursuit [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Data stream clustering [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Computer Science::Sound 0202 electrical engineering electronic engineering information engineering Trigonometric functions 020201 artificial intelligence & image processing Invariant (mathematics) dictionary learning Cluster analysis Neural coding Gradient descent Dictionary learning Algorithm |
| Zdroj: | Machine Learning and Knowledge Discovery in Databases ISBN: 9783030109240 ECML/PKDD (1) ECML/PKDD, 2018 ECML/PKDD, 2018, Sep 2018, Dublin, Ireland |
| DOI: | 10.1007/978-3-030-10925-7_22 |
| Popis: | 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 \(l_0\) sparse coding problem is formalised and a time warp invariant orthogonal matching pursuit based on a new cosine maximisation time warp operator is proposed. A dictionary learning under time warp is then formalised and a gradient descent solution is developed. Lastly, a time series clustering based on the time warp sparse coding and dictionary learning is presented. The proposed approach is evaluated and compared to major alternative methods on several public datasets, with an application to deezer music data stream clustering. Data related to this paper are available at: The link to the data and the evaluating algorithms are provided in the paper. Code related to this paper is available at: The link will be provided at the first author personal website (http://ama.liglab.fr/~varasteh/). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|