Abstract
We present an algorithm, Sparse Atomic Feature Learning (SAFL), that transforms noisy labeled datasets into a sparse domain by learning atomic features of the underlying signal space via gradient minimization. The sparse signal representations are highly compressed and cleaner than the original signals. We demonstrate the effectiveness of our techniques on fMRI activity patterns. We produce low-dimensional, sparse representations which achieve over 98% compression of the original signals. The transformed signals can be used to classify left-out testing data at a higher accuracy than the initial data.
Original language | English (US) |
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Title of host publication | 2014 IEEE Signal Processing in Medicine and Biology Symposium, IEEE SPMB 2014 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781479981847 |
DOIs | |
State | Published - 2014 |
Externally published | Yes |
Event | 2014 IEEE Signal Processing in Medicine and Biology Symposium, IEEE SPMB 2014 - Philadelphia, United States Duration: Dec 13 2014 → Dec 13 2014 |
Publication series
Name | 2014 IEEE Signal Processing in Medicine and Biology Symposium, IEEE SPMB 2014 - Proceedings |
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Other
Other | 2014 IEEE Signal Processing in Medicine and Biology Symposium, IEEE SPMB 2014 |
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Country/Territory | United States |
City | Philadelphia |
Period | 12/13/14 → 12/13/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.