GAUTAM V. PENDSE

 
 

Selected Publications


Machine learning related

  1. Gautam V. Pendse, "BSVM: A Banded Suport Vector Machine" (2011), arXiv:1107.2347v1 [stat.ML] Go to BSVM Project Webpage

  2. Gautam V. Pendse, "A tutorial on the LASSO and the shooting algorithm" (2011), pdf Go to LASSO Shooting Webpage

  3. Gautam V. Pendse, "PMOG: The projected mixture of Gaussians model with application to blind source separation" (2010), Neural Networks (in press), doi:10.1016/j.neunet.2011.12.005 [download preprint at arXiv:1008.2743v1] Go to PMOG Project Webpage

Medical Imaging related

  1. Gautam Pendse, David Borsook and Lino Becerra, "A simple and objective method for reproducible resting state network (RSN) detection in fMRI" (2011), arXiv:1108.2248v1 [stat.AP] Go to RAICAR_N Project Webpage

  2. Gautam Pendse, David Borsook and Lino Becerra. "Enhanced false discovery rate using Gaussian mixture models for thresholding fMRI statistical maps" (2009), NeuroImage, 47(1), pp 231-261. [doi:10.1016/j.neuroimage.2009.02.035]

  3. Gautam Pendse, Richard Baumgartner, Adam Schwarz, Alexandre Coimbra, David Borsook and Lino Becerra, "A statistical framework for optimal design matrix generation with application to fMRI" (2010), IEEE Transactions on Medical Imaging, doi:10.1109/TMI.2010.2044512, IEEE version
    [download preprint at arXiv:0903.1880v1]

  4. Gautam Pendse, Adam Schwarz, Richard Baumgartner, Alexandre Coimbra, David Borsook and Lino Becerra, "Robust, unbiased general linear model estimation of phMRI amplitude in the presence of variation in the temporal response profile" (2010), Journal of Magnetic Resonance Imaging, 31(6), pp 1445-57. [doi:10.1002/jmri.22180]

  5. Gautam Pendse, David Borsook and Lino Becerra, "ADIS: A robust pursuit algorithm for probabilistic, constrained and non-square blind source separation with application to fMRI" (2009), arXiv:0902.4879v1 [stat.CO]

Non-Medical Imaging related

  1. H. D. Mittelmann, G. Pendse, D. E. Rivera, and H. Lee. "Optimization-based Design of Plant-Friendly Multisine Signals using Geometric Discrepancy Criteria" (2007), Computational Optimization and Applications, 38, pp 173-190 Preprint [Link]

  2. H. Lee, D. E. Rivera, H. D. Mittelmann, and G. Pendse. "Optimization-based Design of Plant-Friendly Input Signals for Model-On-Demand Estimation and Model Predictive Control" (2007), in Proceedings of American Control Conference (ACC), pp 1560-1565 Preprint [Link]

  3. D. E. Rivera, H. Lee, H. D. Mittelmann, and G. Pendse. "Optimization-based Design of Plant-Friendly Multisine Signals using Geometric Discrepancy Criteria" (2006), 14th IFAC Symposium on System Identification (SYSID 2006), Newcastle, Australia, March 29-31 Preprint

  4. D. E. Rivera, H. Lee, H. D. Mittelmann, and G. Pendse, "Optimization-based Design of Plant-Friendly Input Signals for Data-Centric Estimation and Control", Annual AIChE Meeting, paper 242k, Cincinnati, OH, October 31 - November 4, 2005 Preprint [CAST Directors' Award Honorable Mention]

  5. H. D. Mittelmann and G. Pendse, Optimal Input Signal Design in Data-Centric System Identification, in Modern Mathematical Models, Methods and Algorithms for Real World Systems, A.H. Siddiqi, I.Duff, and O.Christensen (eds.), Anamaya Publishers, New Delhi-London, 2006, pp. 14-59. Preprint [Book chapter]