@conference {12492,
	title = {Robust regression using sparse learning for high dimensional parameter estimation problems},
	booktitle = {Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on},
	year = {2010},
	month = {2010/03//},
	pages = {3846 - 3849},
	abstract = {Algorithms such as Least Median of Squares (LMedS) and Random Sample Consensus (RANSAC) have been very successful for low-dimensional robust regression problems. However, the combinatorial nature of these algorithms makes them practically unusable for high-dimensional applications. In this paper, we introduce algorithms that have cubic time complexity in the dimension of the problem, which make them computationally efficient for high-dimensional problems. We formulate the robust regression problem by projecting the dependent variable onto the null space of the independent variables which receives significant contributions only from the outliers. We then identify the outliers using sparse representation/learning based algorithms. Under certain conditions, that follow from the theory of sparse representation, these polynomial algorithms can accurately solve the robust regression problem which is, in general, a combinatorial problem. We present experimental results that demonstrate the efficacy of the proposed algorithms. We also analyze the intrinsic parameter space of robust regression and identify an efficient and accurate class of algorithms for different operating conditions. An application to facial age estimation is presented.},
	keywords = {algorithm;random, analysis;, combinatorial, complexity;least, complexity;parameter, consensus;robust, Estimation, estimation;polynomials;regression, learning;sparse, median, of, problem;cubic, problem;polynomial, problem;sparse, regression, representation;computational, sample, squares;parameter, TIME},
	doi = {10.1109/ICASSP.2010.5495830},
	author = {Mitra, K. and Veeraraghavan,A. and Chellapa, Rama}
}
@conference {15713,
	title = {An adaptive ESPRIT based on URV decomposition},
	booktitle = {Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on},
	volume = {4},
	year = {1993},
	month = {1993/04//},
	pages = {37 -40 vol.4 - 37 -40 vol.4},
	abstract = {ESPRIT is an algorithm for determining the fixed directions of arrival of a set of narrowband signals at an array of sensors. Its computational burden makes it unsuitable for real-time processing of signals with time-varying directions of arrival. The authors develop a new implementation of ESPRIT that has potential for real-time processing. It is based on a rank-revealing URV decomposition, rather than the eigendecomposition or singular value decomposition (SVD) used in previous ESPRIT algorithms. Its performance is demonstrated on simulated data representing both constant and time-varying signals. It is shown that the URV-based ESPRIT algorithm is effective for estimating time-varying directions-of-arrival at considerable computational savings over the SVD-based algorithm.<<ETX>>},
	keywords = {algorithm;array, complexity;parameter, decomposition;real-time, ESPRIT, estimation;real-time, filters;array, of, processing;adaptive, processing;computational, savings;performance;rank-revealing, sensors;computational, signal, systems;, systems;time-varying, URV, URV-based},
	doi = {10.1109/ICASSP.1993.319588},
	author = {Liu,K. J.R and O{\textquoteright}Leary, Dianne P. and Stewart, G.W. and Wu,Y.-J.J.}
}
