Manifold Learning From Time Series

We apply our manifold learning algorithm to synthetic data and real world applications. The experiment on synthetic data clearly demonstrates that by taking temporal dependency among global coordinates into consideration our proposed algorithm achieves superior learning results than other manifold learning algorithms that treat samples in the training data set as independent, identical, distributed (i.i.d). In addition, we demonstrate that our algorithm is capable of solving complicated real world problems including appearance-based object tracking and robot map learning.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Lin, Ruei-Sung

Contributors dc:contributor

• Levinson, Stephen E.

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI3223657

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81722