抄録
High-dimensional data require a lot of computational work for processing. In order to efficiently and accurately obtain the results of measurement from high-dimensional data, significant features should be extracted before processing. Here we present a feature extraction method for significance weighted supervised classification. Conventional methods of extracting features consider only the average classification accuracy. In contrast with this, we present a new method of feature extraction which is based on a significance weighted criterion. The purpose is to extract features which separate particular pair of classes and give high classification accuracy for important classes.
In the first step, assuming that all the classes have the same within-class covariance matrices and normally distributed, all the data are reduced by principal component analysis. Most of the information can be expressed in low dimensional space because many of the dimensions of high-dimensional data are highly correlated. In the next step, feature vectors are determined in the space of reduced data. Each feature is extracted successively by selecting the feature vectors which separate the hardest-to-separate pair of classes among the important pairs of classes to be separated. A feature vector which separates two classes is set according to the Fisher's linear discriminant function.
The method is applied to about 500 dimensional hyperspectral data which are required to be classified into five categories. The feature extraction technique, together with the results of numerical simulations which confirm the validity of this approach are presented.
