Work

Multi-task feature learning by using trace norm regularization

Year: 2017

Type: article

Abstract: Multi-task learning can extract the correlation of multiple related machine learning problems to improve performance. This paper considers applying the multi-task learning method to learn a single task. We propose a new learning approach, which employs the mixture of expert model to divide a learning task into several related sub-tasks, and then uses the trace norm regularization to extract common feature representation of these sub-tasks. A nonlinear extension of this approach by using kernel is also provided. Experiments conducted on both simulated and real data sets demonstrate the advantage of the proposed approach.

Source: Open Physics

DOI: 10.1515/phys-2017-0079

Authors: Zhang Jiangmei, Yu Binfeng, Ji Haibo, Kunpeng Wang

Institutions: University of Science and Technology of China, Southwest University of Science and Technology

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,356

OpenAlex ID: W2769719897

FWCI:

Cites: 27

Cited by: 4

Related to: 10