Multi-task manifold learning for small sample size datasets

TL;DR

This paper introduces a multi-task manifold learning approach that enhances performance on small sample datasets by leveraging instance and model transfer, and can generate new samples for existing and new tasks.

Contribution

It presents a novel multi-task manifold learning method combining instance and model transfer, suitable for small sample sizes and capable of generating new samples.

Findings

Successfully estimated manifolds with very few samples

Improved performance on artificial and face image datasets

Effective in generating new samples for tasks

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.