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Proceedings Paper

Feature extraction for deep neural networks based on decision boundaries
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Paper Abstract

Feature extraction is a process used to reduce data dimensions using various transforms while preserving the discriminant characteristics of the original data. Feature extraction has been an important issue in pattern recognition since it can reduce the computational complexity and provide a simplified classifier. In particular, linear feature extraction has been widely used. This method applies a linear transform to the original data to reduce the data dimensions. The decision boundary feature extraction method (DBFE) retains only informative directions for discriminating among the classes. DBFE has been applied to various parametric and non-parametric classifiers, which include the Gaussian maximum likelihood classifier (GML), the k-nearest neighbor classifier, support vector machines (SVM) and neural networks. In this paper, we apply DBFE to deep neural networks. This algorithm is based on the nonparametric version of DBFE, which was developed for neural networks. Experimental results with the UCI database show improved classification accuracy with reduced dimensionality.

Paper Details

Date Published: 1 May 2017
PDF: 6 pages
Proc. SPIE 10203, Pattern Recognition and Tracking XXVIII, 1020306 (1 May 2017); doi: 10.1117/12.2263172
Show Author Affiliations
Seongyoun Woo, Yonsei Univ. (Korea, Republic of)
Chulhee Lee, Yonsei Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 10203:
Pattern Recognition and Tracking XXVIII
Mohammad S. Alam, Editor(s)

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