Dataset Open Access

Hathi Trust Library Vectorized features

Benjamin M. Schmidt


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  <identifier identifierType="DOI">10.5281/zenodo.1424831</identifier>
  <creators>
    <creator>
      <creatorName>Benjamin M. Schmidt</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-1142-5720</nameIdentifier>
      <affiliation>Northeastern University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Hathi Trust Library Vectorized features</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>Dimensionality reduction</subject>
    <subject>Digital Libraries</subject>
    <subject>Digital humanities</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-09-21</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1424831</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1424830</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;A smaller-resolution (and therefore more portable) version of the Stable Random Projection Hathi Trust features described in my forthcoming article. The Northeastern repository is many individual files with 1280 random dimensions; this is just 640 random dimensions. The numbers are also experimentally encoded as half-precision floats, which cuts the file size by half at the cost of only being supported by my Python module. The net result is a file 1/4 the size of the full resolution ones for the paper that has, probably, something like 60-80% of the information content.&lt;/p&gt;

&lt;p&gt;The full file is &amp;#39;&lt;a href="https://www.zenodo.org/api/files/6d615dbd-65de-4391-93ac-91b302bb57e4/ht-640d-complete-half-precision.bin?versionId=0e9f5551-0888-454c-8bb8-0dd3f3d6d949"&gt;ht-640d-complete-half-precision.bin&lt;/a&gt;&amp;#39;. You can also download 11 smaller files organized by language.&lt;/p&gt;

&lt;p&gt;Code to read these files is at&amp;nbsp;https://github.com/bmschmidt/pySRP.&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
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