A New Method for Dimensionality Reduction based on Multivariate Feature Fusion

Dimensionality reduction is the process of mapping high-dimension patterns to a lower dimension subspace. When done prior to classification, estimates obtained in the lower dimension subspace are more reliable. We propose a novel method based on graphical multivariate feature fusion and use it to offer a visual representation of high dimensional data. The graphical processing method we propose, relies on using a multilayered structure of feature fusion which produces as output of the lower dimensional representation. We implement feature fusion by combining method of feature selection and feature extraction. Experiments on the data set of machine learning database indicate the novel method we propose provides better representation than Fisher's linear discriminant (FLD) and some other nonlinear methods of dimensionality reduction that are often used.