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dc.contributor.authorEravci, B.en_US
dc.contributor.authorFerhatosmanoglu, H.en_US
dc.date.accessioned2019-02-21T16:05:38Z
dc.date.available2019-02-21T16:05:38Z
dc.date.issued2018en_US
dc.identifier.issn1041-4347
dc.identifier.urihttp://hdl.handle.net/11693/50264
dc.description.abstractWe 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.
dc.description.sponsorshipThis study was funded in part by The Scientific and Technological Research Council of Turkey (TUBITAK) under grant EEEAG 111E217. The authors thank the data curators of [44] for providing such comprehensive set.
dc.language.isoEnglish
dc.source.titleIEEE Transactions on Knowledge and Data Engineeringen_US
dc.relation.isversionofhttps://doi.org/10.1109/TKDE.2018.2820119
dc.subjectAutoencodersen_US
dc.subjectDiversityen_US
dc.subjectRelevance feedbacken_US
dc.subjectTime series analysisen_US
dc.titleDiverse relevance feedback for time series with autoencoder based summarizationsen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineering
dc.citation.spage2298en_US
dc.citation.epage2311en_US
dc.citation.volumeNumber30en_US
dc.citation.issueNumber12en_US
dc.relation.projectEEEAG 111E217 - Council for Scientific and Industrial Research, CSIR
dc.identifier.doi10.1109/TKDE.2018.2820119
dc.publisherIEEE Computer Society


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