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• W4309763130 abstract "Semi-supervised learning has become a hot learning framework, where large amounts of unlabeled data and small amounts of labeled data are available during the training process. The recently proposed Laplacian least squares twin support vector machine (Lap-LSTSVM) is an excellent tool to solve the semi-supervised classification problem. Motivated by the success of Lap-LSTSVM, in this paper, we propose a novel Laplacian Lp norm least squares twin support vector machine (Lap-LpLSTSVM). There are several advantages of our proposed method: (1) The performance of our proposed Lap-LpLSTSVM can be improved by the adjustability of the value of p. (2) The introduced Lp norm graph regularization term can efficiently exploit the geometric information embedded in the data. (3) An efficient iterative strategy is employed to solve the optimization problem. Besides, to demonstrate that our proposed method can make use of unlabeled data effectively, least squares twin support vector machine (LSTSVM) which only uses the same labeled data is used to compare with our proposed method. The experimental results on both synthetic and real-world datasets show that our proposed method outperforms other state-of-the-art methods and can also deal with noisy datasets." @default.
• W4309763130 created "2022-11-29" @default.
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• W4309763130 date "2023-04-01" @default.
• W4309763130 modified "2023-10-16" @default.
• W4309763130 title "Laplacian Lp norm least squares twin support vector machine" @default.
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• W4309763130 doi "https://doi.org/10.1016/j.patcog.2022.109192" @default.
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