Application of Geometric Algebra to Clustering of Questionnaire Data

DOI

Bibliographic Information

Other Title
  • アンケートデータのクラスタリングへのGeometric Algebraの適用

Description

Design of similarity between instances is important for many machine learning methods. Especially the kernel matrix, also known as the Gram matrix, plays a central role in the kernel machines such as support vector machine. As far as we know, however, design of kernels for the case where each instance is given by m-tuple of n-dimensional vectors has not been established. Geometric algebra (GA) is a generalization of complex numbers and of quaternions, and it is able to describe spatial objects and relations between them. In this study we introduce GA to extract geometric features from m-tuples of n-dimensional vectors. Then we evaluate kernel matrices induced from the geometric features under kernel alignment between them. We also apply a semi-supervised learning based on the kernels to analysis of questionnaire.

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Keywords

Details 詳細情報について

  • CRID
    1390001205666240512
  • NII Article ID
    130005035202
  • DOI
    10.14864/fss.24.0.174.0
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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