Optimization in Signal Processing and Big Data

Traditional convex optimization methods and signal processing techniques can be employed in big data problems characterized by large amounts of data in high-dimensional spaces. However, big data brings additional difficulties that need to be taken care of such as high-computational cost that require simple and efficient methods, distributed implementations, faulty data in the form of outliers, etc.

Tutorial Paper

• Ying Sun, Prabhu Babu, and Daniel P. Palomar, “Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning,” IEEE Trans. on Signal Processing, vol. 65, no. 3, pp. 794-816, Feb. 2017.
  🏆 2020 Young Author Best Paper Award by the IEEE Signal Processing Society

Other Papers

• Arnaud Breloy, Sandeep Kumar, Ying Sun, and Daniel P. Palomar, “Majorization-Minimization on the Stiefel Manifold with Application to Robust Sparse PCA,” IEEE Trans. on Signal Processing, vol. 69, pp. 1507-1520, Feb. 2021.

• Konstantinos Benidis, Ying Sun, Prabhu Babu, and Daniel P. Palomar, “Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation,” IEEE Trans. on Signal Processing, vol. 64, no. 23, pp. 6211-6226, Dec. 2016. [R package sparseEigen] [Matlab code]

• Licheng Zhao, Prabhu Babu, and Daniel P. Palomar, “Efficient Algorithms on Robust Low-Rank Matrix Completion Against Outliers,” IEEE Trans. on Signal Processing, vol. 64, no. 18, pp. 4767- 4780, Sept. 2016.

• Yang Yang, Marius Pesavento, Mengyi Zhang, and Daniel P. Palomar, “An Online Parallel Algorithm for Recursive Estimation of Sparse Signals,” IEEE Trans. on Signal and Inform. Proc. Over Networks, vol. 2, no. 3, pp. 290-305, Sept. 2016.

• Yang Yang, Gesualdo Scutari, Daniel P. Palomar, and Marius Pesavento, “A Parallel Decomposition Method for Nonconvex Stochastic Multi-Agent Optimization Problems,” IEEE Trans. on Signal Processing, vol. 64, no. 11, pp. 2949-2964, June 2016.

• Yiyong Feng and Daniel P. Palomar, “Normalization of Linear Support Vector Machines,” IEEE Trans. on Signal Processing, vol. 63, no. 17, pp. 4673-4688, Sept. 2015.

• Ying Sun, Prabhu Babu, and Daniel P. Palomar, “Regularized Robust Estimation of Mean and Covariance Matrix Under Heavy-Tailed Distributions,” IEEE Trans. on Signal Processing, vol. 63, no. 12, pp. 3096-3109, June 2015. [Matlab code] [R package fitHeavyTail]

• Junxiao Song, Prabhu Babu, and Daniel P. Palomar, “Sparse Generalized Eigenvalue Problem via Smooth Optimization,” IEEE Trans. on Signal Processing, vol. 63, no. 7, pp. 1627-1642, April 2015. [Matlab code]

• Ying Sun, Prabhu Babu, and Daniel P. Palomar, “Regularized Tyler’s Scatter Estimator: Existence, Uniqueness, and Algorithms,” IEEE Trans. on Signal Processing, vol. 62, no. 19, pp. 5143-5156, Oct. 2014. [R package fitHeavyTail]

• Gesualdo Scutari, Francisco Facchinei, Peiran Song, Daniel P. Palomar, and Jong-Shi Pang, “Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems,” IEEE Trans. on Signal Processing, vol. 62, no. 3, pp. 641-656, Feb. 2014.
  🏆 2015 Young Author Best Paper Award by the IEEE Signal Processing Society