The AKRON-Kalman filter for tracking time-varying networks

Victor Carluccio, Nidhal Bouaynaya, Gregory Ditzler, Hassan M. Fathallah-Shaykh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We propose the AKRON-Kalman filter for the problem of inferring sparse dynamic networks from a noisy undersampled set of measurements. Unlike the Lasso-Kalman filter, which uses regularization with the l1-norm to find an approximate sparse solution, the AKRON-Kalman tracker uses the l1 approximation to find the location of a 'sufficient number' of zero entries that guarantees the existence of the optimal sparsest solution. This sufficient number of zeros can be shown to be exactly equal to the dimension of the kernel of an under-determined system. The AKRON-Kalman tracker then iteratively refines this solution of the l1 problem by ensuring that the observed reconstruction error does not exceed the measurement noise level. The AKRON solution is sparser, by construction, than the Lasso solution while the Kalman tracking ensures that all past observations are taken into account to estimate the network in any given stage. The AKRON-Kalman tracker is applied to the inference of the time-varying wing-muscle genetic regulatory network of the Drosophila Melanogaster (fruit fly) during the embryonic, larval, pupal and adulthood phases. Unlike all previous approaches, the proposed AKRON-Kalman was able to recover all reportedly known interactions in the Flybase dataset.

Original languageEnglish (US)
Title of host publication2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages313-316
Number of pages4
ISBN (Electronic)9781509041794
DOIs
StatePublished - Apr 11 2017
Event4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017 - Orlando, United States
Duration: Feb 16 2017Feb 19 2017

Publication series

Name2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017

Conference

Conference4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
CountryUnited States
CityOrlando
Period2/16/172/19/17

Keywords

  • Compressive sensing
  • Convex optimization
  • L1-reconstruction
  • Time-varying genomic regulatory networks

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

Fingerprint Dive into the research topics of 'The AKRON-Kalman filter for tracking time-varying networks'. Together they form a unique fingerprint.

  • Cite this

    Carluccio, V., Bouaynaya, N., Ditzler, G., & Fathallah-Shaykh, H. M. (2017). The AKRON-Kalman filter for tracking time-varying networks. In 2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017 (pp. 313-316). [7897268] (2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BHI.2017.7897268