Efficient sequential feature selection based on adaptive eigenspace model
Though Fisher score is a representative and effective feature selection method, it has an unsolved drawback: it either evaluates the features individually and selects the top features, or selects features using the sequential search strategies. The individual-method ignores the mutual relationship among the selected features while the sequential-methods always suffer from heavy computation. In this work, we present an efficient sequential feature selection method. In the proposed method, the generalized Fisher score is used as a robust measurement of the discriminative ability of the features, which can naturally deal with the Small Size Sample problem. Besides, each feature is considered as a pattern vector and an adaptive eigenspace model is applied to update the generalized Fisher score. In the proposed adaptive eigenspace model, the size of the eigen-decomposition problems does not increase with the number of selected features, but is determined by the dimension of the adaptive eignespace. If the dimension of the adaptive eigenspace model is fixed, the proposed algorithm approximately consumes constant time to evaluate a candidate feature. Therefore, the proposed method is computationally more efficient than the traditional sequential methods. Experiments on six widely used face databases are conducted to demonstrate the efficacy of the proposed approach.
feature selection eigenspace model fisher score incremental learning