  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.
