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dc.contributor.authorÄyrämö, Sami
dc.contributor.authorPölönen, Ilkka
dc.contributor.authorEskelinen, Matti
dc.date.accessioned2017-10-31T10:43:10Z
dc.date.available2017-10-31T10:43:10Z
dc.date.issued2017
dc.identifier.citationÄyrämö, S., Pölönen, I., & Eskelinen, M. (2017). Clustering Incomplete Spectral Data with Robust Methods. In E. Honkavaara, B. Hu, K. Karantzalos, X. Liang, R. Müller, E. Nocerino, . . . , & P. Rönnholm (Eds.), <em>ISPRS SPEC3D 2017 : Frontiers in Spectral imaging and 3D Technologies for Geospatial Solutions</em> (pp. 13-17). International archives of the photogrammetry, remote sensing and spatial information sciences, Volume XLII-3/W3. International Society for Photogrammetry and Remote Sensing. <a href="https://doi.org/10.5194/isprs-archives-XLII-3-W3-13-2017">doi:10.5194/isprs-archives-XLII-3-W3-13-2017</a>
dc.identifier.otherTUTKAID_75451
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/55740
dc.description.abstractMissing value imputation is a common approach for preprocessing incomplete data sets. In case of data clustering, imputation methods may cause unexpected bias because they may change the underlying structure of the data. In order to avoid prior imputation of missing values the computational operations must be projected on the available data values. In this paper, we apply a robust nan-K-spatmed algorithm to the clustering problem on hyperspectral image data. Robust statistics, such as multivariate medians, are more insensitive to outliers than classical statistics relying on the Gaussian assumptions. They are, however, computationally more intractable due to the lack of closed-form solutions. We will compare robust clustering methods on the bands incomplete data cubes to standard K-means with full data cubes.
dc.language.isoeng
dc.publisherInternational Society for Photogrammetry and Remote Sensing
dc.relation.ispartofISPRS SPEC3D 2017 : Frontiers in Spectral imaging and 3D Technologies for Geospatial Solutions
dc.relation.ispartofseriesInternational archives of the photogrammetry, remote sensing and spatial information sciences;Volume XLII-3/W3
dc.relation.urihttps://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W3/13/2017/isprs-archives-XLII-3-W3-13-2017.pdf
dc.subject.otherrobust
dc.subject.otherclustering
dc.subject.otherspectral data
dc.subject.otherinterpolation
dc.subject.otherK-means
dc.subject.othernan-K-spatmed
dc.titleClustering Incomplete Spectral Data with Robust Methods
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201710264075
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikka
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2017-10-26T12:15:15Z
dc.type.coarconference paper
dc.description.reviewstatuspeerReviewed
dc.format.pagerange13-17
dc.relation.issn1682-1750
dc.type.versionpublishedVersion
dc.rights.copyright© Authors 2017. This is an open access article distributed under the terms of a Creative Commons License.
dc.rights.accesslevelopenAccessfi
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.5194/isprs-archives-XLII-3-W3-13-2017


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© Authors 2017. This is an open access article distributed under the terms of a Creative Commons License.
Except where otherwise noted, this item's license is described as © Authors 2017. This is an open access article distributed under the terms of a Creative Commons License.