Incremental feature extraction based on decision boundaries. (May 2018) - Woo, Seongyoun; Lee, Chulhee
Highlights: We developed a gradient based decision boundary feature extraction algorithm for neural networks and its incremental version. The proposed method updates the decision boundaries from sequentially added samples and obtains discriminately informative vectors based on the updated decision boundaries. When applied to real world databases, it showed noticeably better classification performance than some existing incremental algorithms. Abstract: Feature extraction is a key algorithm to solve the dimensionality problem. Most feature extraction algorithms use a batch mode, which requires all data available at the same time to calculate new features. Recently, with more available data and advancements of transmission technology, the need for incremental algorithms has increased. In this paper, we propose a gradient descent DBFE method (GDDBFE) that shows a substantial improvement in processing time. Based on this GDDBFE, we then propose an incremental gradient descent decision boundary feature extraction method (IGDDBFE). The proposed IGDDBFE method consists of two steps: updating the decision boundaries and adding discriminately informative features with newly added samples and then updating the feature vectors by incremental eigenvector updates. Experiments with real-world databases show that the proposed method shows improved performance compared to some existing methods.