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JACIII Vol.15 No.6 pp. 662-670
doi: 10.20965/jaciii.2011.p0662
(2011)

Paper:

Feature Extraction with Space Folding Model and its Application to Machine Learning

Minh Tuan Pham*, Tomohiro Yoshikawa*, Takeshi Furuhashi*,
and Kanta Tachibana**

*Department of Computational Science and Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

**Department of Information Design, Faculty of Informatics, Kogakuin University, 1-24-2 Nishi-Shinjuku, Tokyo 163-8677, Japan

Received:
December 20, 2010
Accepted:
May 3, 2011
Published:
August 20, 2011
Keywords:
machine learning, feature extraction, space folding model, space folding vector
Abstract

Feature extraction provides an essential element in most machine learning methods, including supervised learning with neural networks. Linearly inseparable data distributions are often non-linearly transformed in some way to make them more linearly separable in the feature space. In this paper, we propose a method of feature extraction with a space folding model. In the proposed method, each basis vector in the m-dimensional data space is divided in the positive and negative directions to optimize it with 2m m-dimensional vectors as variables. 2m variable vectors are estimated to minimize the cross entropy of class labels and distances so that instances in the same classes are gathered closer together and those in other classes are separated farther apart. The proposed method, in which linear transformation is applied to each quadrant to collectively realize a nonlinear transformation, is expected to lead to improvements in accuracy of discrimination over conventional methods of feature extraction using single linear transformations. In this paper, we have confirmed the effectiveness of the proposed method of feature extraction with a space folding model on a UCI benchmark problem.

Cite this article as:
M. Pham, T. Yoshikawa, T. Furuhashi, and <. Tachibana, “Feature Extraction with Space Folding Model and its Application to Machine Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.6, pp. 662-670, 2011.
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Last updated on Sep. 09, 2019