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Full-Text Articles in Social and Behavioral Sciences
Efficient Structured Support Vector Regression, Ke Jia, Lei Wang, Nianjun Liu
Efficient Structured Support Vector Regression, Ke Jia, Lei Wang, Nianjun Liu
Faculty of Engineering and Information Sciences - Papers: Part A
Support Vector Regression (SVR) has been a long standing problem in machine learning, and gains its popularity on various computer vision tasks. In this paper, we propose a structured support vector regression framework by extending the max-margin principle to incorporate spatial correlations among neighboring pixels. The objective function in our framework considers both label information and pairwise features, helping to achieve better cross-smoothing over neighboring nodes. With the bundle method, we effectively reduce the number of constraints and alleviate the adverse effect of outliers, leading to an efficient and robust learning algorithm. Moreover, we conduct a thorough analysis for the …
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. …