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xiaolinolan's Projects

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Fused group lasso regularized multi-task feature learning code. We refer to the paper for details about the model and the optimization algorithms: Xiaoli Liu, Peng Cao, Jianzhong Wang, Jun Kong, Dazhe Zhao. Fused Group Lasso Regularized Multi-Task Feature Learning and Its Application to the Cognitive Performance Prediction of Alzheimer’s Disease[J]. Neuroinformatics, 2018: 1-24. DOI=10.1007/s12021-018-9398-5

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Fused Laplacian Sparse Group Lasso code. We refer to the paper for details about the model and the optimization algorithms: Xiaoli Liu, Peng Cao, André R. Goncalvesd, Dazhe Zhao, Arindam Banerjee. Modeling Alzheimer’s Disease Progression with Fused Laplacian Sparse Group Lasso[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2018, 12(6): 65. DOI=10.1145/3230668

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Group Guided Sparse Group Lasso Multi-task Learning code. We refer to the paper for details about the model and the optimization algorithms: Xiaoli Liu, Peng Cao, Jinzhu Yang, Dazhe Zhao, Osmar Zaiane. Group Guided Sparse Group Lasso Multi-task Learning for Cognitive Performance Prediction of Alzheimer’s Disease[C]. International Conference on Brain Informatics. Springer, Cham, 2017: 202-212. DOI=https://doi.org/10.1007/978-3-319-70772-3_19

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Multikernel Multitask Learning code. We refer to the paper for details about the model and the optimization algorithms: Xiaoli Liu, Peng Cao, Jinzhu Yang, Dazhe Zhao. Linearized and Kernelized Sparse Multitask Learning for Predicting Cognitive Outcomes in Alzheimer’s Disease[J]. Computational and mathematical methods in medicine, 2018. DOI=10.1155/2018/7429782

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Multi-task Sparse Group Lasso code. We refer to the paper for details about the model and the optimization algorithms: Xiaoli Liu, André R. Goncalvesd, Peng Cao, Dazhe Zhao, Arindam Banerjee. Modeling Alzheimer's disease cognitive scores using multi-task sparse group lasso[J]. Computerized Medical Imaging and Graphics, 2018, 66: 100-114. DOI=10.1016/j.compmedimag.2017.11.001

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