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                <term xml:lang="en">reduction dimension</term>
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              <p>In the context of vineyard soils characterizationn this paper explores and compare dierent recent Non Linear Dimensionality Reduction (NLDR) methods on a high-dimensional Near InfraRed Spectroscopy (NIRS) dataset. NLDR methods are based on k-neighborhood criterion and Euclidean and fractional distances metrics are tested. Results show that Multiscale Jensen-Shannon Embedding (Ms JSE) coupled with eu-clidean distance outperform all over methods. Application on data is made at global scale and at dierent scale of depth of soil.</p>
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          <date type="end">2017-12-31</date>
          <desc>
            <address>
              <addrLine>3, rue de Valois 75001 Paris</addrLine>
              <country key="FR"/>
            </address>
          </desc>
        </org>
        <org type="institution" xml:id="struct-92114" status="OLD">
          <idno type="ROR">https://ror.org/01x3gbx83</idno>
          <orgName>Institut National de la Recherche Agronomique</orgName>
          <orgName type="acronym">INRA</orgName>
          <date type="start">1946-05-18</date>
          <date type="end">2019-12-31</date>
          <desc>
            <address>
              <country key="FR"/>
            </address>
            <ref type="url">http://www.inra.fr</ref>
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</TEI>