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.
|Number of pages||10|
|Publication status||Published - 2018 May|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2015R1A2A2A01006421 ).
© 2017 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence