Multi-task manifold learning for small sample size datasets
Multi-task manifold learning for small sample size datasets
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DOI：
10.1016/j.neucom.2021.11.043
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Hideaki Ishibashi;Kazushi Higa;T. Furukawa
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文献类型：
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作者：
Hideaki Ishibashi;Kazushi Higa;T. Furukawa
在这项研究中，我们开发了一种多任务流形学习的方法。该方法旨在提高多任务流形学习的性能，特别是当每个任务具有少量样本时。此外，该方法还旨在为新任务生成新样本，以及为现有任务生成新样本。在所提出的方法中，我们使用两种不同类型的信息传输：实例传输和模型传输。例如，数据集在类似的任务之间合并，而对于模型转移，流形模型在类似的任务之间平均。为此，所提出的方法由一组生成流形模型对应的任务，这是集成到一个一般的模型的纤维束。我们将该方法应用于人工数据集和人脸图像集，结果表明，该方法能够估计流形，即使是少量的样本。
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.