Feature extraction for deep neural networks based on decision boundaries
2017
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.
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