In this paper, we propose the regularized discriminant entropy (RDE) which considers both class information and scatter information on original data. Based on the results of maximizing the RDE, we develop a supervised feature extraction algorithm called regularized discriminant entropy analysis (RDEA). RDEA is quite simple and requires no approximation in theoretical derivation. The experiments with several publicly available data sets show the feasibility and effectiveness of the proposed algorithm with encouraging results.
- Discriminant entropy analysis
- Entropy-based learning
- Regularized discriminant entropy
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence