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• W3215690986 abstract "In this study, we develop a method for multi-task manifold learning. The method aims to improve the performance of manifold learning for multiple tasks, particularly when each task has a small number of samples. Furthermore, the method also aims to generate new samples for new tasks, in addition to new samples for existing tasks. In the proposed method, we use two different types of information transfer: instance transfer and model transfer. For instance transfer, datasets are merged among similar tasks, whereas for model transfer, the manifold models are averaged among similar tasks. For this purpose, the proposed method consists of a set of generative manifold models corresponding to the tasks, which are integrated into a general model of a fiber bundle. We applied the proposed method to artificial datasets and face image sets, and the results showed that the method was able to estimate the manifolds, even for a tiny number of samples." @default.
• W3215690986 created "2021-12-06" @default.
• W3215690986 creator A5036658960 @default.
• W3215690986 creator A5086750113 @default.
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• W3215690986 date "2022-02-01" @default.
• W3215690986 modified "2023-09-25" @default.
• W3215690986 title "Multi-task manifold learning for small sample size datasets" @default.
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• W3215690986 doi "https://doi.org/10.1016/j.neucom.2021.11.043" @default.
• W3215690986 hasPublicationYear "2022" @default.
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