Feature extraction for deep neural networks based on decision boundaries

Seongyoun Woo, Chulhee Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationPattern Recognition and Tracking XXVIII
ISBN (Electronic)9781510609075
Publication statusPublished - 2017 Jan 1
EventPattern Recognition and Tracking XXVIII 2017 - Anaheim, United States
Duration: 2017 Apr 122017 Apr 13


OtherPattern Recognition and Tracking XXVIII 2017
CountryUnited States


All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Woo, S., & Lee, C. (2017). Feature extraction for deep neural networks based on decision boundaries. In Pattern Recognition and Tracking XXVIII (Vol. 10203). [1020306] SPIE. https://doi.org/10.1117/12.2263172