Abstract
In fields of classification and clustering of patterns most conventional methods of feature extraction do not pay much attention to the geometric properties of data, even in cases where the data have spatial features. This paper proposes to use geometric algebra to systematically extract geometric features from data given in a vector space. We show the results of classification of handwritten digits and those of clustering of consumers’ impression with the proposed method.
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Pham, M.T., Tachibana, K., Hitzer, E.M.S., Yoshikawa, T., Furuhashi, T. (2010). Classification and Clustering of Spatial Patterns with Geometric Algebra. In: Bayro-Corrochano, E., Scheuermann, G. (eds) Geometric Algebra Computing. Springer, London. https://doi.org/10.1007/978-1-84996-108-0_12
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DOI: https://doi.org/10.1007/978-1-84996-108-0_12
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