DeepAI AI Chat
Log In Sign Up

Multi-task manifold learning for small sample size datasets

by   Hideaki Ishibashi, et al.
Kyushu Institute of Technology

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.


page 12

page 13


VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization

Invariance to diverse types of image corruption, such as noise, blurring...

Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP

Multi-task reinforcement learning is a rich paradigm where information f...

Trace norm regularization for multi-task learning with scarce data

Multi-task learning leverages structural similarities between multiple t...

Combining datasets to increase the number of samples and improve model fitting

For many use cases, combining information from different datasets can be...

Grassmann Manifold Flow

Recently, studies on machine learning have focused on methods that use s...

An Iterative Approach for Multiple Instance Learning Problems

Multiple Instance learning (MIL) algorithms are tasked with learning how...

Spatial regression-based transfer learning for prediction problems

Although spatial prediction is widely used for urban and environmental m...