Clustering Incomplete Spectral Data with Robust Methods
Äyrämö, 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, I. Pölönen, & P. Rönnholm (Eds.), ISPRS SPEC3D 2017 : Frontiers in Spectral imaging and 3D Technologies for Geospatial Solutions (pp. 13-17). International Society for Photogrammetry and Remote Sensing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W3. https://doi.org/10.5194/isprs-archives-XLII-3-W3-13-2017
Julkaistu sarjassa
International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesToimittajat
Hu, B. |
Päivämäärä
2017Tekijänoikeudet
© Authors 2017. This is an open access article distributed under the terms of a Creative Commons License.
Missing 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.
Julkaisija
International Society for Photogrammetry and Remote SensingKonferenssi
Congress of the International Society for Photogrammetry and Remote SensingKuuluu julkaisuun
ISPRS SPEC3D 2017 : Frontiers in Spectral imaging and 3D Technologies for Geospatial SolutionsISSN Hae Julkaisufoorumista
1682-1750
Alkuperäislähde
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W3/13/2017/isprs-archives-XLII-3-W3-13-2017.pdfJulkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/27306858
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