We propose a preprocessing method to improve the performance of Principal Component Analysis (PCA) for classification\r\nproblems composed of two steps; in the first step, the weight of each feature is calculated by using a feature weighting method.\r\nThen the features with weights larger than a predefined threshold are selected. The selected relevant features are then subject to\r\nthe second step. In the second step, variances of features are changed until the variances of the features are corresponded to their\r\nimportance. By taking the advantage of step 2 to reveal the class structure, we expect that the performance of PCA increases in\r\nclassification problems. Results confirm the effectiveness of our proposed methods.
Loading....