Selective Feature Generation Method for Classi?cation of Low-dimensional Data

摘要

We propose a method that generates input features to e?ectively classify low-dimensional data. To do this, we ?rst generate high-order terms for the input features of the original low-dimensional data to form a candidate set of new input features. Then, thediscriminationpowerofthecandidateinputfeaturesisquantitatively evaluated by calculating the ‘discrimination distance’ for each candidate feature. As a result, only candidates with a large amount of discriminative information are selected to create a new input feature vector, and the discriminant features that are to be used as input to the classi?er are extracted from the new input feature vectors by using a subspace discriminant analysis. Experiments on low-dimensional data sets in the UCI machine learning repository and several kinds of low-resolution facial image data show that the proposed method improves the classi?cation performance of low-dimensional data by generating features