A multi-task learning method using gradient descent with applications
Date
2021
Authors
Larson, Nathan Dean, author
Azimi-Sadjadi, Mahmood R., advisor
Pezeshki, Ali, committee member
Oprea, Iuliana, committee member
Journal Title
Journal ISSN
Volume Title
Abstract
There is a critical need to develop classification methods that can robustly and accurately classify different objects in varying environments. Each environment in a classification problem can contain its own unique challenges which prevent traditional classifiers from performing well. To solve classification problems in different environments, multi-task learning (MTL) models have been applied that define each environment as a separate task. We discuss two existing MTL algorithms and explain how they are inefficient for situations involving high-dimensional data. A gradient descent-based MTL algorithm is proposed which allows for high-dimensional data while providing accurate classification results. Additionally, we introduce a kernelized MTL algorithm which may allow us to generate nonlinear classifiers. We compared our proposed MTL method with an existing method, Efficient Lifelong Learning Algorithm (ELLA), by using them to train classifiers on the underwater unexploded ordnance (UXO) and extended modified National Institute of Standards and Technology (EMNIST) datasets. The UXO dataset contained acoustic color features of low-frequency sonar data. Both real data collected from physical experiments as well as synthetic data were used forming separate environments. The EMNIST digits dataset contains grayscale images of handwritten digits. We used this dataset to show how our proposed MTL algorithm performs when used with more tasks than are in the UXO dataset. Our classification experiments showed that our gradient descent-based algorithm resulted in improved performance over those of the traditional methods. The UXO dataset had a small improvement while the EMNIST dataset had a much larger improvement when using our MTL algorithm compared to ELLA and the single task learning method.