TY - JOUR AU - de Carvalho, Tiago Buarque Assunção AU - Sibaldo, Maria Aparecida Amorim AU - Tsang, Ing Ren AU - Cavalcanti, George Darmiton da Cunha PY - 2017/11/27 Y2 - 2024/03/28 TI - Principal Component Analysis for Supervised Learning: a minimum classification error approach JF - Journal of Information and Data Management JA - JIDM VL - 8 IS - 2 SE - KDMiLe 2016 DO - 10.5753/jidm.2017.1613 UR - https://sol.sbc.org.br/journals/index.php/jidm/article/view/1613 SP - 131 AB - <p>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.</p> ER -