A New Feature Selection Method to Extract Functional Structures from Multidimensional Symbolic Data

Yujiro ONO; Manabu ICHINO

A New Feature Selection Method to Extract Functional Structures from Multidimensional Symbolic Data

Yujiro ONO, Manabu ICHINO

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In this paper, we propose a feature selection method to extract functional structures embedded in multidimensional data. In our approach, we do not approximate functional structures directly. Instead, we focus on the seemingly trivial property that functional structures are geometrically thin in an informative subspace. Using this property, we can exclude irrelevant features to describe functional structures. As a result, we can use conventional identification methods, which use only informative features, to accurately identify functional structures. In this paper, we define Geometrical Thickness (GT) in the Cartesian System Model (CSM), a mathematical model that can manipulate symbolic data. Additionally, we define Total Geometrical Thickness (TGT) which expresses geometrical structures in data. Using TGT, we investigate a new feature selection method and show its capabilities by applying it to two sets of artificial and one set of real data.

Publication: IEICE TRANSACTIONS on Information Vol.E81-D No.6 pp.556-564

Publication Date: 1998/06/25

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Category: Image Processing,Computer Graphics and Pattern Recognition

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Yujiro ONO, Manabu ICHINO, "A New Feature Selection Method to Extract Functional Structures from Multidimensional Symbolic Data" in IEICE TRANSACTIONS on Information, vol. E81-D, no. 6, pp. 556-564, June 1998, doi: .
Abstract: In this paper, we propose a feature selection method to extract functional structures embedded in multidimensional data. In our approach, we do not approximate functional structures directly. Instead, we focus on the seemingly trivial property that functional structures are geometrically thin in an informative subspace. Using this property, we can exclude irrelevant features to describe functional structures. As a result, we can use conventional identification methods, which use only informative features, to accurately identify functional structures. In this paper, we define Geometrical Thickness (GT) in the Cartesian System Model (CSM), a mathematical model that can manipulate symbolic data. Additionally, we define Total Geometrical Thickness (TGT) which expresses geometrical structures in data. Using TGT, we investigate a new feature selection method and show its capabilities by applying it to two sets of artificial and one set of real data.
URL: https://global.ieice.org/en_transactions/information/10.1587/e81-d_6_556/_p

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@ARTICLE{e81-d_6_556,
author={Yujiro ONO, Manabu ICHINO, },
journal={IEICE TRANSACTIONS on Information},
title={A New Feature Selection Method to Extract Functional Structures from Multidimensional Symbolic Data},
year={1998},
volume={E81-D},
number={6},
pages={556-564},
abstract={In this paper, we propose a feature selection method to extract functional structures embedded in multidimensional data. In our approach, we do not approximate functional structures directly. Instead, we focus on the seemingly trivial property that functional structures are geometrically thin in an informative subspace. Using this property, we can exclude irrelevant features to describe functional structures. As a result, we can use conventional identification methods, which use only informative features, to accurately identify functional structures. In this paper, we define Geometrical Thickness (GT) in the Cartesian System Model (CSM), a mathematical model that can manipulate symbolic data. Additionally, we define Total Geometrical Thickness (TGT) which expresses geometrical structures in data. Using TGT, we investigate a new feature selection method and show its capabilities by applying it to two sets of artificial and one set of real data.},
keywords={},
doi={},
ISSN={},
month={June},}

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TY - JOUR
TI - A New Feature Selection Method to Extract Functional Structures from Multidimensional Symbolic Data
T2 - IEICE TRANSACTIONS on Information
SP - 556
EP - 564
AU - Yujiro ONO
AU - Manabu ICHINO
PY - 1998
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E81-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 1998
AB - In this paper, we propose a feature selection method to extract functional structures embedded in multidimensional data. In our approach, we do not approximate functional structures directly. Instead, we focus on the seemingly trivial property that functional structures are geometrically thin in an informative subspace. Using this property, we can exclude irrelevant features to describe functional structures. As a result, we can use conventional identification methods, which use only informative features, to accurately identify functional structures. In this paper, we define Geometrical Thickness (GT) in the Cartesian System Model (CSM), a mathematical model that can manipulate symbolic data. Additionally, we define Total Geometrical Thickness (TGT) which expresses geometrical structures in data. Using TGT, we investigate a new feature selection method and show its capabilities by applying it to two sets of artificial and one set of real data.
ER -