AffiliationUniv Arizona, Dept Elect & Comp Engn
MetadataShow full item record
CitationDitzler, G., Bouaynaya, N., Shterenberg, R., & Fathallah-Shaykh, H. M. (2019). Approximate kernel reconstruction for time-varying networks. BioData mining, 12(1), 5.
Rights© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.
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AbstractTo address the problem of inferring sparse time-varying networks from a set of under-sampled measurements, we propose the Approximate Kernel RecONstruction (AKRON) Kalman filter. AKRON supersedes the Lasso regularization by starting from the Lasso-Kalman inferred network and judiciously searching the space for a sparser solution. We derive theoretical bounds for the optimality of AKRON. We evaluate our approach against the Lasso-Kalman filter on synthetic data. The results show that not only does AKRON-Kalman provide better reconstruction errors, but it is also better at identifying if edges exist within a network. Furthermore, we perform a real-world benchmark on the lifecycle (embryonic, larval, pupal, and adult stages) of the Drosophila Melanogaster.
NoteOpen access journal
VersionFinal published version
SponsorsNational Science Foundation (NSF) [CCF-1527822, DUE-1610911]
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