@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}
}
@article {18167,
	title = {Anti-collusion fingerprinting for multimedia},
	journal = {Signal Processing, IEEE Transactions on},
	volume = {51},
	year = {2003},
	month = {2003/04//},
	pages = {1069 - 1087},
	abstract = {Digital fingerprinting is a technique for identifying users who use multimedia content for unintended purposes, such as redistribution. These fingerprints are typically embedded into the content using watermarking techniques that are designed to be robust to a variety of attacks. A cost-effective attack against such digital fingerprints is collusion, where several differently marked copies of the same content are combined to disrupt the underlying fingerprints. We investigate the problem of designing fingerprints that can withstand collusion and allow for the identification of colluders. We begin by introducing the collusion problem for additive embedding. We then study the effect that averaging collusion has on orthogonal modulation. We introduce a tree-structured detection algorithm for identifying the fingerprints associated with K colluders that requires O(Klog(n/K)) correlations for a group of n users. We next develop a fingerprinting scheme based on code modulation that does not require as many basis signals as orthogonal modulation. We propose a new class of codes, called anti-collusion codes (ACCs), which have the property that the composition of any subset of K or fewer codevectors is unique. Using this property, we can therefore identify groups of K or fewer colluders. We present a construction of binary-valued ACC under the logical AND operation that uses the theory of combinatorial designs and is suitable for both the on-off keying and antipodal form of binary code modulation. In order to accommodate n users, our code construction requires only O( radic;n) orthogonal signals for a given number of colluders. We introduce three different detection strategies that can be used with our ACC for identifying a suspect set of colluders. We demonstrate the performance of our ACC for fingerprinting multimedia and identifying colluders through experiments using Gaussian signals and real images.},
	keywords = {(mathematics);, additive, algorithm;, and, anti-collusion, attack;, averaging, binary, code, codes;, codevectors;, coding;, colluders, collusion;, combinatorial, communication;, compression;, correlation;, cost-effective, data, data;, design, DETECTION, detection;, digital, embedding;, fingerprinting;, Gaussian, identification;, image, images;, keying;, logical, mathematics;, Modulation, modulation;, multimedia, multimedia;, of, on-off, operation;, orthogonal, processes;, real, redistribution;, Security, signal, signals;, theory;, tree-structured, TREES, watermarking;},
	isbn = {1053-587X},
	doi = {10.1109/TSP.2003.809378},
	author = {Trappe,W. and M. Wu and Wang,Z.J. and Liu,K. J.R}
}
@conference {18166,
	title = {Anti-collusion codes: multi-user and multimedia perspectives},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {2},
	year = {2002},
	month = {2002///},
	pages = {II-149 - II-152 vol.2 - II-149 - II-152 vol.2},
	abstract = {Digital fingerprinting is an effective method to identify users who might try to redistribute multimedia content, such as images and video. These fingerprints are typically embedded into the content using watermarking techniques that are designed to be robust to a variety of attacks. A cheap and effective attack against such digital fingerprints is collusion, where several differently marked copies of the same content are averaged or combined to disrupt the underlying fingerprint. We present a construction of collusion-resistant fingerprints based upon anti-collusion codes (ACC) and binary code modulation. ACC have the property that the composition of any subset of K or fewer codevectors is unique. Using this property, we build fingerprints that allow for the identification of groups of K or less colluders. We present a construction of binary-valued ACC under the logical AND operation using the theory of combinatorial designs. Our code construction requires only Oscr;( radic;n) orthogonal signals to accommodate n users. We demonstrate the performance of our ACC for fingerprinting multimedia by identifying colluders through experiments using real images.},
	keywords = {and, anti-collusion, authentication;, binary, code, codes;, coding;, combinatorial, computing;, content;, data, designs;, digital, embedding;, encapsulation;, fingerprinting;, image, images;, logical, mathematics;, message, Modulation, modulation;, multimedia, operation;, performance;, watermarking;},
	doi = {10.1109/ICIP.2002.1039909},
	author = {Trappe,W. and M. Wu and Liu,K. J.R}
}
@article {15000,
	title = {Optimal algorithms on the pipelined hypercube and related networks},
	journal = {Parallel and Distributed Systems, IEEE Transactions on},
	volume = {4},
	year = {1993},
	month = {1993/05//},
	pages = {582 - 591},
	abstract = {Parallel algorithms for several important combinatorial problems such as the all nearest smaller values problem, triangulating a monotone polygon, and line packing are presented. These algorithms achieve linear speedups on the pipelined hypercube, and provably optimal speedups on the shuffle-exchange and the cube-connected-cycles for any number <e1>p </e1> of processors satisfying 1 les;<e1>p</e1> les;<e1>n</e1>/((log<sup>3</sup><e1>n</e1>)(loglog <e1>n</e1>)<sup>2</sup>), where <e1>n</e1> is the input size. The lower bound results are established under no restriction on how the input is mapped into the local memories of the different processors},
	keywords = {algorithms;pipeline, algorithms;pipelined, combinatorial, geometry;parallel, hypercube;shuffle-exchange;combinatorial, mathematics;computational, packing;monotone, polygon;parallel, problems;cube-connected-cycles;line, processing;},
	isbn = {1045-9219},
	doi = {10.1109/71.224210},
	author = {JaJa, Joseph F. and Ryu,K. W.}
}
