Multi-View Representation Learning: Approaches and Applications

Doctoral Dissertation

Abstract

The performance of machine learning algorithms is heavily dependent on the quality of the data representation. Learning discriminative data representations becomes a critical step in the machine learning task pipeline. Data representations can be obtained from multiple views. For example, in a complex network model, the representation of each node can be viewed as a combination of the correlations between itself and its neighbors and the node attributes. A comprehensive representation can be obtained because data from different views usually contain complementary information.

This dissertation tackles the problem of multi-view representation learning. How to generate the data representations by incorporating multiple aspects of data, such as text, network topological information, and data derived from various sources. Multi-view representations are capable of uncovering the implicit and complex explanatory factors of variation behind the data.

Attributes

Attribute NameValues
Author Pingjie Tang
Contributor Nitesh V. Chawla, Research Director
Degree Level Doctoral Dissertation
Degree Discipline Computer Science and Engineering
Degree Name Doctor of Philosophy
Banner Code
  • PHD-CSE

Defense Date
  • 2020-07-30

Submission Date 2020-08-20
Record Visibility Public
Content License
  • All rights reserved

Departments and Units
Catalog Record

Files

Please Note: You may encounter a delay before a download begins. Large or infrequently accessed files can take several minutes to retrieve from our archival storage system.