Augmented dictionary learning for motion prediction
Developing accurate models and efficient representations of multivariate trajectories is important for understanding the behavior patterns of mobile agents. This work presents a dictionary learning algorithm for developing a part-based trajectory representation, which combines merits of the existing...
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| Format: | Article |
| Language: | en_US |
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2016
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| Online Access: | http://hdl.handle.net/1721.1/105795 https://orcid.org/0000-0003-3756-3256 https://orcid.org/0000-0002-1648-8325 https://orcid.org/0000-0001-8576-1930 |
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| author | Chen, Yu Fan Liu, Miao How, Jonathan P |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Chen, Yu Fan Liu, Miao How, Jonathan P |
| author_sort | Chen, Yu Fan |
| collection | MIT |
| description | Developing accurate models and efficient representations of multivariate trajectories is important for understanding the behavior patterns of mobile agents. This work presents a dictionary learning algorithm for developing a part-based trajectory representation, which combines merits of the existing Markovian-based and clustering-based approaches. In particular, this work presents the augmented semi-nonnegative sparse coding (ASNSC) algorithm for solving a constrained dictionary learning problem, and shows that the proposed method would converge to a local optimum given a convexity condition. We consider a trajectory modeling application, in which the learned dictionary atoms correspond to local motion patterns. Classical semi-nonnegative sparse coding approaches would add dictionary atoms with opposite signs to reduce the representational error, which can lead to learning noisy dictionary atoms that correspond poorly to local motion patterns. ASNSC addresses this problem and learns a concise set of intuitive motion patterns. ASNSC shows significant improvement over existing trajectory modeling methods in both prediction accuracy and computational time, as revealed by extensive numerical analysis on real datasets. |
| first_indexed | 2024-09-23T12:01:01Z |
| format | Article |
| id | mit-1721.1/105795 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:01:01Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | mit-1721.1/1057952022-09-27T23:32:47Z Augmented dictionary learning for motion prediction Chen, Yu Fan Liu, Miao How, Jonathan P Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Chen, Yu Fan Liu, Miao How, Jonathan P Developing accurate models and efficient representations of multivariate trajectories is important for understanding the behavior patterns of mobile agents. This work presents a dictionary learning algorithm for developing a part-based trajectory representation, which combines merits of the existing Markovian-based and clustering-based approaches. In particular, this work presents the augmented semi-nonnegative sparse coding (ASNSC) algorithm for solving a constrained dictionary learning problem, and shows that the proposed method would converge to a local optimum given a convexity condition. We consider a trajectory modeling application, in which the learned dictionary atoms correspond to local motion patterns. Classical semi-nonnegative sparse coding approaches would add dictionary atoms with opposite signs to reduce the representational error, which can lead to learning noisy dictionary atoms that correspond poorly to local motion patterns. ASNSC addresses this problem and learns a concise set of intuitive motion patterns. ASNSC shows significant improvement over existing trajectory modeling methods in both prediction accuracy and computational time, as revealed by extensive numerical analysis on real datasets. 2016-12-12T20:00:31Z 2016-12-12T20:00:31Z 2016-05 Article http://purl.org/eprint/type/ConferencePaper 978-1-4673-8026-3 http://hdl.handle.net/1721.1/105795 Chen, Yu Fan, Miao Liu, and Jonathan P. How. “Augmented Dictionary Learning for Motion Prediction.” IEEE, 2016. 2527–2534. https://orcid.org/0000-0003-3756-3256 https://orcid.org/0000-0002-1648-8325 https://orcid.org/0000-0001-8576-1930 en_US http://dx.doi.org/10.1109/ICRA.2016.7487407 IEEE International Conference on Robotics and Automation, 2016. ICRA '16. Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf MIT web domain |
| spellingShingle | Chen, Yu Fan Liu, Miao How, Jonathan P Augmented dictionary learning for motion prediction |
| title | Augmented dictionary learning for motion prediction |
| title_full | Augmented dictionary learning for motion prediction |
| title_fullStr | Augmented dictionary learning for motion prediction |
| title_full_unstemmed | Augmented dictionary learning for motion prediction |
| title_short | Augmented dictionary learning for motion prediction |
| title_sort | augmented dictionary learning for motion prediction |
| url | http://hdl.handle.net/1721.1/105795 https://orcid.org/0000-0003-3756-3256 https://orcid.org/0000-0002-1648-8325 https://orcid.org/0000-0001-8576-1930 |
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