Principal Component Analysis for Supervised Learning: a minimum classification error approach
Tiago Buarque Assunção de Carvalho, Maria Aparecida Amorim Sibaldo, Tsang Ing Ren, George Darmiton da Cunha Cavalcanti · Cadernos de Linguística e Teoria da Literatura (Universidade Federal de Minas Gerais) · 2017
We present an alternative method to use Principal Component Analysis (PCA) for supervised learning. The proposed method extract features similarly to PCA but the features are selected by minimizing the Bayes error rate for classification. We show that the proposed method selects features that best separate the elements of the different classes. Using real and synthetic datasets, along with four different classifiers, experimental results show that the recognition accuracy of the proposed technique is improved compared to PCA.