Incremental Nonparametric Weighted Feature Extraction for OnlineSubspace Pattern Classification

TL;DR

This paper introduces INWFE, an online, nonparametric feature extraction method that improves classification accuracy and reduces computation time by emphasizing boundary points and handling data incrementally.

Contribution

The paper presents INWFE, an incremental version of NWFE, enabling asynchronous data addition and improved performance on complex, non-Gaussian datasets.

Findings

INWFE converges to NWFE accuracy after training.

INWFE reduces execution time compared to NWFE.

Effective on diverse multidimensional datasets.

Abstract

In this paper, a new online method based on nonparametric weighted feature extraction (NWFE) is proposed. NWFE was introduced to enjoy optimum characteristics of linear discriminant analysis (LDA) and nonparametric discriminant analysis (NDA) while rectifying their drawbacks. It emphasizes the points near decision boundary by putting greater weights on them and deemphasizes other points. Incremental nonparametric weighted feature extraction (INWFE) is the online version of NWFE. INWFE has advantages of NWFE method such as extracting more than L-1 features in contrast to LDA. It is independent of the class distribution and performs well in complex distributed data. The effects of outliers are reduced due to the nature of its nonparametric scatter matrix. Furthermore, it is possible to add new samples asynchronously, i.e. whenever a new sample becomes available at any given time, it can be added to the algorithm. This is useful for many real world applications since all data cannot be available in advance. This method is implemented on Gaussian and non-Gaussian multidimensional data, a number of UCI datasets and Indian Pine dataset. Results are compared with NWFE in terms of classification accuracy and execution time. For nearest neighbour classifier it shows that this technique converges to NWFE at the end of learning process. In addition, the computational complexity is reduced in comparison with NWFE in terms of execution time.