Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Chen, Ying-Nong | |
dc.contributor.author | Wang, Yu-Chen | |
dc.contributor.author | Han, Chin-Chuan | |
dc.contributor.author | Fan, Kuo-Chin | |
dc.contributor.editor | Skala, Václav | |
dc.date.accessioned | 2018-05-17T07:19:49Z | - |
dc.date.available | 2018-05-17T07:19:49Z | - |
dc.date.issued | 2015 | |
dc.identifier.citation | WSCG '2015: short communications proceedings: The 23rd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2015 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech Republic8-12 June 2015, p. 75-82. | en |
dc.identifier.isbn | 978-80-86943-66-4 | |
dc.identifier.issn | 2464-4617 | |
dc.identifier.uri | wscg.zcu.cz/WSCG2015/CSRN-2502.pdf | |
dc.identifier.uri | http://hdl.handle.net/11025/29668 | |
dc.description.abstract | Nearest feature line (NFL) embedding (NFLE) is an eigenspace transformation algorithm based on the NFL strategy. Based on this strategy, the NFLE algorithm generates a low dimensional space in which the local structures of samples in the original high dimensional space are preserved. Though NFLE has successfully demonstrated its discriminative capability, the non-linear manifold structure cannot be structured more efficiently by linear scatters using the linear NFLE method. To address this, a general NFLE transformation, called fuzzy/kernel NFLE, is proposed for feature extraction in which kernelization and fuzzification are simultaneously considered. In the proposed scheme, samples are projected into a kernel space and assigned larger weights based on that of their neighbors according to their neighbors. In that way, not only is the non-linear manifold structure preserved, but also are the discriminative powers of classifiers increased. The proposed method is compared with various state-of-the-art methods to evaluate the performance by several benchmark data sets. From the experimental results, the proposed FKNFLE outperformed the other, more conventional, methods. | en |
dc.format | 8 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | Václav Skala - UNION Agency | en |
dc.relation.ispartofseries | WSCG '2015: short communications proceedings | en |
dc.rights | © Václav Skala - UNION Agency | cs |
dc.subject | hyperspektrální klasifikace obrazů | cs |
dc.subject | rozmanité učení | cs |
dc.subject | kernelizace | cs |
dc.subject | fuzzifikace | cs |
dc.title | Hyperspectral image xlassification using a general NFLE transformation with kernelization and fuzzification | en |
dc.type | konferenční příspěvek | cs |
dc.type | conferenceObject | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.subject.translated | hyperspectral image classification | en |
dc.subject.translated | manifold learning | en |
dc.subject.translated | kernelization | en |
dc.subject.translated | fuzzification | en |
dc.type.status | Peer-reviewed | en |
Vyskytuje se v kolekcích: | WSCG '2015: Short Papers Proceedings |
Soubory připojené k záznamu:
Soubor | Popis | Velikost | Formát | |
---|---|---|---|---|
Chen.pdf | Plný text | 284,44 kB | Adobe PDF | Zobrazit/otevřít |
Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam:
http://hdl.handle.net/11025/29668
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