Linear Principal Component Discriminant Analysis

We propose a series of data analysis methods for both supervised and un-supervised learning techniques. Three objectives of data relationship and characteristics are used to establish a uniform framework of our proposed methods, which are inspired by principal component analysis and linear discriminant analysis. By using the three objectives and some combinations of them, we investigate and illustrate the performance of the proposed methods. We use simulation data and classical Iris data to investigate the proposed methods. Some discoveries and issues are analysed and discussed arising from the evaluation results. The advantages of the proposed framework do not only depend on its explanation capability of data relationship, but also depend on the fusion of multiple data projection techniques. We investigate some potential research issues of the proposed methods. Some works which extend the current study with kernel method are analysed theoretically. We also present some characteristics of the proposal and discuss some open opportunities and future works.