Publications

For a full list of publications please visit Professor Pourkamali-Anaraki’s Google Scholar page.

Preprints

Refereed Publications

  • M. A. Hariri-Ardebili and F. Pourkamali-Anaraki, “Matrix Completion for Cost Reduction in Finite Element Simulations Under Hybrid Uncertainties,” Applied Mathematics Modeling, accepted.


  • F. Pourkamali-Anaraki, S. Becker, and M. B. Wakin, “Randomized Clustered Nystrom for Large-Scale Kernel Machines,” Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pages 3960–3967, 2018.


  • M. A. Hariri-Ardebili and F. Pourkamali-Anaraki, “Support Vector Machine Based Reliability Analysis of Concrete Dams,” Soil Dynamics and Earthquake Engineering, vol. 104, pages 276–295, 2018.


  • M. A. Hariri-Ardebili and F. Pourkamali-Anaraki, “Simplified Reliability Analysis of Multi Hazard Risk in Gravity Dams via Machine Learning Techniques,” Archives of Civil and Mechanical Engineering, vol. 18, no. 2, pages 592–610, 2018.


  • F. Pourkamali-Anaraki and S. Becker, “Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means,” IEEE Transactions on Information Theory, vol. 63, no. 5, pages 2954–2974, 2017.


  • F. Pourkamali-Anaraki and S. Becker, “A Randomized Approach to Efficient Kernel Clustering,” IEEE Global Conference on Signal and Information Processing (GlobalSIP), pages 207–211, 2016.


  • F. Pourkamali-Anaraki, “Estimation of the Sample Covariance Matrix from Compressive Measurements,” IET Signal Processing, vol. 10, no. 9, pages 1089–1095, 2016.


  • F. Pourkamali-Anaraki, S. Becker, and S. Hughes, “Efficient Dictionary Learning via Very Sparse Random Projections,” Sampling Theory and Applications (SampTA), pages 478–482, 2015.


  • F. Pourkamali-Anaraki and S. Hughes, “Memory and Computation Efficient PCA via Very Sparse Random Projections,” Proceedings of the 31st International Conference on Machine Learning (ICML), pages 1341–1349, 2014.


  • F. Pourkamali-Anaraki and S. Hughes, “Efficient Recovery of Principal Components from Compres- sive Measurements with Application to Gaussian Mixture Model Estimation,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 2332–2336, 2014.


  • F. Pourkamali-Anaraki and S. Hughes, “Kernel Compressive Sensing,” IEEE International Conference on Image Processing (ICIP), pages 494–498, 2013.


  • F. Pourkamali-Anaraki and S. Hughes, “Compressive K-SVD,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 5469–5473, 2013.