Conventional feature extraction methods extract features from the viewpoint of the best average separability among the classes. Lots of studies on feature extraction for classification attempt to classify all the classes included. However sometimes it is more important and significant to recognize certain specific classes (prior classes) rather than to discriminate all the classes. Such non-discriminatory computing greatly increase the computing time, to that end, we hope to propose a new algorithm, The extracted features can discriminate the prior class from others and separate each of the classes as much as possible at the same time. The subjective introduction based method was proposed, which the subjective significance was introduced into the feature extraction for the classification with priority in the paper, We will rename its subjective proposed feature extraction method. The experimental results show that the method can obtain more effective features compared to conventional linear discriminant analysis (LDA) methods.
Keywords: Informaiton, feature extraction, separability, classification with priority
