Incremental feature extraction based on decision boundaries
作者：
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.
摘要
•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.
论文关键词：Dimensionality reduction,Incremental learning,Neural networks,Decision boundary feature extraction
论文评审过程：Received 26 May 2017, Revised 14 November 2017, Accepted 9 December 2017, Available online 11 December 2017, Version of Record 27 December 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.12.010
