3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Directional Statistics in Machine Learning: a Brief Review

      Preprint

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The modern data analyst must cope with data encoded in various forms, vectors, matrices, strings, graphs, or more. Consequently, statistical and machine learning models tailored to different data encodings are important. We focus on data encoded as normalized vectors, so that their "direction" is more important than their magnitude. Specifically, we consider high-dimensional vectors that lie either on the surface of the unit hypersphere or on the real projective plane. For such data, we briefly review common mathematical models prevalent in machine learning, while also outlining some technical aspects, software, applications, and open mathematical challenges.

          Related collections

          Author and article information

          Journal
          2016-05-01
          Article
          1605.00316
          53ad8f4c-adcf-493e-80b0-3f48501f1ae9

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          History
          Custom metadata
          12 pages, slightly modified version of submitted book chapter
          stat.ML

          Machine learning
          Machine learning

          Comments

          Comment on this article