Interpretable time series kernel analytics by pre-image estimation

Autor: Thi Phuong Thao Tran, Saeed Varasteh Yazdi, Paul Honeine, Patrick Gallinari, Ahlame Douzal-Chouakria
Přispěvatelé: Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Equipe Apprentissage (DocApp - LITIS), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Normandie Université (NU), 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), ANR-18-CE23-0014,APi,Apprivoiser la Pré-image(2018)
Rok vydání: 2020
Předmět:
Linguistics and Language
Time series
Computer science
Feature vector
02 engineering and technology
[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]
Representation learning
Language and Linguistics
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
[INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY]
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
Artificial Intelligence
020204 information systems
0202 electrical engineering
electronic engineering
information engineering
Time series averaging
Series (mathematics)
business.industry
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Pre-image problem
Dictionary learning
Kernel machinery
Metric space
Transformation (function)
Kernel method
Analytics
Kernel (statistics)
Embedding
020201 artificial intelligence & image processing
Kernel PCA
business
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Algorithm
Zdroj: Artificial Intelligence
Artificial Intelligence, Elsevier, 2020, 286, pp.103342. ⟨10.1016/j.artint.2020.103342⟩
ISSN: 0004-3702
DOI: 10.1016/j.artint.2020.103342
Popis: International audience; Kernel methods are known to be effective to analyse complex objects by implicitly embedding them into some feature space. To interpret and analyse the obtained results, it is often required to restore in the input space the results obtained in the feature space, by using pre-image estimation methods. This work proposes a new closed-form pre-image estimation method for time series kernel analytics that consists of two steps. In the first step, a time warp function, driven by distance constraints in the feature space, is defined to embed time series in a metric space where analytics can be performed conveniently. In the second step, the time series pre-image estimation is cast as learning a linear (or a nonlinear) transformation that ensures a local isometry between the time series embedding space and the feature space. The proposed method is compared to the state of the art through three major tasks that require pre-image estimation: 1) time series averaging, 2) time series reconstruction and denoising and 3) time series representation learning. The extensive experiments conducted on 33 publicly-available datasets show the benefits of the pre-image estimation for time series kernel analytics.
Databáze: OpenAIRE
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