I. Introduction
Over the past decades, machine learning methods have already been widely applied in many fields. In general, feature extraction is critical to improve the accuracy and it is also helpful for visualizing and interpreting the data. While, the multi-objective characteristic of feature extraction has usually been ignored. Principal component analysis (PCA) [1], kernel principal component analysis (KPCA) [2], independent component analysis (ICA) [3] are the representatives of feature extraction methods. They project the original feature space into a new subspace to improve the classification accuracy and reduce the feature dimensions. The features extracted by these methods are not interpretable, while the interpretation of the extracted features can contribute to further research in the application.