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oai:www.ideals.illinois.edu:2142/81722 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Levinson, Stephen E. Lin, Ruei-Sung 2015-09-25T20:20:09Z 2015-09-25T20:20:09Z 10000-01-01 2006 2006 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. Made available in DSpace on 2015-09-25T20:20:09Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3223657.pdf: 3059125 bytes, checksum: b9167f32c3ba0d0b26cf8cd930ff8c28 (MD5) Previous issue date: 2006 Embargo set by: Seth Robbins for item 83003 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 133 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. http://hdl.handle.net/2142/81722 (MiAaPQ)AAI3223657 eng Computer Science Manifold Learning From Time Series text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.