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 Feature Extractions with Geometric Algebra for Classification of Objects
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Tác giả hoặc Nhóm tác giả: Minh Tuan Pham, Kanta Tachibana, Eckhard Hitzer, Sven　Buchholz, Tomohiro Yoshikawa, and Takeshi Furuhashi
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Nơi đăng: IEEE World Congress on Computational Intelligence / International Joint Conference on Neural Networks
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; Số: 978-1-4244-1820-6;Từ->đến trang: 4070-4074;Năm: 2008
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
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. In this study we introduce geometric algebra to undertake various kinds of feature extraction from spatial data. Geometric algebra is a generalization of complex numbers and of quaternions, and it is able to describe spatial objects and relations between them. 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 hand-written digits, which were classified by feature extraction with the proposed method.
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ABSTRACT
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. In this study we introduce geometric algebra to undertake various kinds of feature extraction from spatial data. Geometric algebra is a generalization of complex numbers and of quaternions, and it is able to describe spatial objects and relations between them. 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 hand-written digits, which were classified by feature extraction with the proposed method.
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