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In this study, we develop a method for multi-task manifold learning. Themethod aims to improve the performance of manifold learning for multiple tasks,particularly when each task has a small number of samples. Furthermore, themethod also aims to generate new samples for new tasks, in addition to newsamples for existing tasks. In the proposed method, we use two different typesof information transfer: instance transfer and model transfer. For instancetransfer, datasets are merged among similar tasks, whereas for model transfer,the manifold models are averaged among similar tasks. For this purpose, theproposed method consists of a set of generative manifold models correspondingto the tasks, which are integrated into a general model of a fiber bundle. Weapplied the proposed method to artificial datasets and face image sets, and theresults showed that the method was able to estimate the manifolds, even for atiny number of samples.

Multi-task manifold learning for small sample size datasets