Effective Distance Based Multi-modality Feature Selection
YE Tingting1, LIU Mingxia1,2, ZHANG Daoqiang1
1.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 2.School of Information Science and Technology, Taishan University, Taian 271021
Abstract Based on the traditional distance measurements, effective distance is adopted to implement feature selection for multi-modality classification. To better reflect the global and local relationships among samples, an effective distance based multi-modality feature selection method is proposed. This method focuses on the global relationship among samples to build model, and effective distance based feature selection learning is realized. Thus, discriminative features are selected. To evaluate the efficiency of the proposed method, experiments are performed on the Alzheimer's disease neuroimaging initiative database and the UCI benchmark database. The experimental results demonstrate that compared with traditional feature selection methods using the Euclidean distance, the proposed method significantly improves the results of multi-modality classification.
About author:: YE Tingting, born in 1990, master. Her research interests include pattern recognition and medical imaging processing.LIU Mingxia, born in 1981, Ph.D., lecturer. Her research interests include machine learning, data mining and medical imaging processing.ZHANG Daoqiang(Corresponding author), born in 1978, Ph.D., professor. His research interests include machine lear-ning, pattern recognition, data mining and medical imaging processing.
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2016, Vol. 29 
