Iterative sparsity score for feature selection and its extension for multimodal data

As a key dimensionality reduction technique in pattern recognition, feature selection has been widely used in information retrieval, text classification and genetic data analysis. In recent years, structural information contained in samples for guiding feature selection has become a new hot spot in machine learning field. Although tremendous feature selection methods have been developed, less important features are still used to construct the structure in those conventional structure based feature selection approaches. In this paper, we propose a new filter-type feature selection method called iterative sparsity score, which is independent of any learning algorithm. The proposed method can preserve the structural information by sparse representation, which can be efficiently solved by a ℓ1-norm minimization problem. To exclude data noise, at one time we discard last m features and iteratively optimize the ℓ1-norm minimization problem. We perform clustering and classification experiments on numerous bench mark datasets. Furthermore, its extension for multimodal data is also developed. We adopt the multi-modality alzheimer's disease data for classification to evaluate the extended method. The experimental results show the effectiveness of our proposed methods compared with several popular feature selection approaches.