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Diverse relevance feedback for time series with autoencoder based summarizations

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Eravci, Bahaeddin and Ferhatosmanoglu, Hakan (2018) Diverse relevance feedback for time series with autoencoder based summarizations. IEEE Transactions on Knowledge and Data Engineering, 30 (12). 2298 -2311. doi:10.1109/TKDE.2018.2820119

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Official URL: http://dx.doi.org/10.1109/TKDE.2018.2820119

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Abstract

We present a relevance feedback based browsing methodology using different representations for time series data. The outperforming representation type, e.g., among dual-tree complex wavelet transformation, Fourier, symbolic aggregate approximation (SAX), is learned based on user annotations of the presented query results with representation feedback. We present the use of autoencoder type neural networks to summarize time series or its representations into sparse vectors, which serves as another representation learned from the data. Experiments on 85 real data sets confirm that diversity in the result set increases precision, representation feedback incorporates item diversity and helps to identify the appropriate representation. The results also illustrate that the autoencoders can enhance the base representations, and achieve comparably accurate results with reduced data sizes.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Algorithms, Time-series analysis -- Classification -- Mathematical models, Time-series analysis -- Forecasting -- Mathematical models, Fourier transformations
Journal or Publication Title: IEEE Transactions on Knowledge and Data Engineering
Publisher: IEEE Computer Society
ISSN: 1041-4347
Official Date: 1 December 2018
Dates:
DateEvent
1 December 2018Published
28 March 2018Available
23 March 2018Accepted
Volume: 30
Number: 12
Page Range: 2298 -2311
DOI: 10.1109/TKDE.2018.2820119
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EEEAG 111E217Türkiye Bilimsel ve Teknolojik Araştirma Kurumuhttp://dx.doi.org/10.13039/501100004410

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