Multi-task manifold learning for small sample size datasets
This addresses the challenge of limited data in multi-task learning scenarios, though it appears incremental as it builds on existing manifold learning techniques.
The paper tackles the problem of manifold learning for multiple tasks with small sample sizes by developing a multi-task method that uses instance and model transfer, and it demonstrates the method's ability to estimate manifolds even with very few samples on artificial and face image datasets.
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.