Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Social and Behavioral Sciences
Efficient Spectral Feature Selection With Minimum Redundancy, Zheng Zhao, Lei Wang, Huan Liu
Efficient Spectral Feature Selection With Minimum Redundancy, Zheng Zhao, Lei Wang, Huan Liu
Faculty of Engineering and Information Sciences - Papers: Part A
Spectral feature selection identifies relevant features by measuring their capability of preserving sample similarity. It provides a powerful framework for both supervised and unsupervised feature selection, and has been proven to be effective in many real-world applications. One common drawback associated with most existing spectral feature selection algorithms is that they evaluate features individually and cannot identify redundant features. Since redundant features can have significant adverse effect on learning performance, it is necessary to address this limitation for spectral feature selection. To this end, we propose a novel spectral feature selection algorithm to handle feature redundancy, adopting an embedded model. …
Hippocampal Shape Classification Using Redundancy Constrained Feature Selection, Luping Zhou, Lei Wang, Chunhua Shen, Nick Barnes
Hippocampal Shape Classification Using Redundancy Constrained Feature Selection, Luping Zhou, Lei Wang, Chunhua Shen, Nick Barnes
Faculty of Engineering and Information Sciences - Papers: Part A
Landmark-based 3D hippocampal shape classification involves high-dimensional descriptor space, many noisy and redundant features, and a very small number of training samples. Feature selection becomes critical in this situation, because it not only improves classification performance, but also identifies the regions that contribute more to shape discrimination. This work identifies the drawbacks of SVM-RFE, and proposes a novel class-separability-based feature selection approach to overcome them. We formulate feature selection as a constrained integer optimization and develop a new algorithm to efficiently and optimally solve this problem. Theoretical analysis and experimental study on both synthetic data and real hippocampus data demonstrate …