Interactive Exploration of Multitask Dependency Networks

Scientists increasingly depend on machine learning algorithms to discover patterns in complex data. Two examples addressed in this dissertation are identifying how information sharing among regions of the brain develops due to learning; and, learning dependency networks of blood proteins associated with cancer. Dependency networks, or graphical models, are learned from the observed data in order to make comparisons between the sub-populations of the dataset. Rarely is there sufficient data to infer robust individual networks for each sub-population. The multiple networks must be considered simultaneously; exploding the hypothesis space of the learning problem. Exploring this complex solution space requires input from the domain scientist to refine the objective function. This dissertation introduces a framework to incorporate domain knowledge in transfer learning to facilitate the exploration of solutions. The framework is a generalization of existing algorithms for multiple network structure identification. Solutions produced with human input narrow down the variance of solutions to those that answer questions of interest to domain scientists. Patterns, such as identifying differences between networks, are learned with higher confidence using transfer learning than through the standard method of bootstrapping. Transfer learning may be the ideal method for making comparisons among dependency networks, whether looking for similarities or differences. Domain knowledge input and visualization of solutions are combined in an interactive tool that enables domain scientists to explore the space of solutions efficiently.

Name thesis:degree_name

Computer Science

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Department of Computer Science

Year

2013

Author dc:creator

• Oyen, Diane

Contributors dc:contributor

• Luger, George
• Lane, Terran
• Eaton, Eric
• Clark, Vincent
• Williams, Lance

• machine learning
• probabilistic graphical models

Language dc:language

English

Identifier

https://digitalrepository.unm.edu/cs_etds/29

OAI identifier oai:identifier

oai:digitalrepository.unm.edu:cs_etds-1028