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Depth first search and linear graph algorithms
 SIAM JOURNAL ON COMPUTING
, 1972
"... The value of depthfirst search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components of an undirect ..."
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Cited by 1384 (19 self)
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The value of depthfirst search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components
Limma: linear models for microarray data
 Bioinformatics and Computational Biology Solutions using R and Bioconductor
, 2005
"... This free opensource software implements academic research by the authors and coworkers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents ..."
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Cited by 759 (13 self)
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This free opensource software implements academic research by the authors and coworkers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents
ELA STRONG LINEAR PRESERVERS OF GTRIDIAGONAL MAJORIZATION ON RN ∗
"... Strong linear preservers of gtridiagonal ..."
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
, 1997
"... We develop a face recognition algorithm which is insensitive to gross variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a highdimensional space. We take advantage of the observation that the images ..."
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Cited by 2263 (18 self)
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of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space  if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce selfshadowing, images will deviate
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
, 2008
"... ..."
Strong linearly independent vectors in semilinear spaces and their applications
"... The aim of this contribution is to discuss the characterizations of Lsemilinear spaces which are generated by strong linearly independent vectors. First, we show that the basis in Lsemilinear spaces which are generated by strong linearly independent vectors is also strong linearly independent. T ..."
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The aim of this contribution is to discuss the characterizations of Lsemilinear spaces which are generated by strong linearly independent vectors. First, we show that the basis in Lsemilinear spaces which are generated by strong linearly independent vectors is also strong linearly independent
Guaranteed minimumrank solutions of linear matrix equations via nuclear norm minimization
, 2007
"... The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints. Such problems have appeared in the literature of a diverse set of fields including system identification and control, Euclidean embedding, and collaborative ..."
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Cited by 568 (23 self)
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The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints. Such problems have appeared in the literature of a diverse set of fields including system identification and control, Euclidean embedding
For Most Large Underdetermined Systems of Linear Equations the Minimal ℓ1norm Solution is also the Sparsest Solution
 Comm. Pure Appl. Math
, 2004
"... We consider linear equations y = Φα where y is a given vector in R n, Φ is a given n by m matrix with n < m ≤ An, and we wish to solve for α ∈ R m. We suppose that the columns of Φ are normalized to unit ℓ 2 norm 1 and we place uniform measure on such Φ. We prove the existence of ρ = ρ(A) so that ..."
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Cited by 560 (10 self)
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We consider linear equations y = Φα where y is a given vector in R n, Φ is a given n by m matrix with n < m ≤ An, and we wish to solve for α ∈ R m. We suppose that the columns of Φ are normalized to unit ℓ 2 norm 1 and we place uniform measure on such Φ. We prove the existence of ρ = ρ(A) so
Representations and constructions of strongly linear fuzzy orderings
 In Proc. EUSFLATESTYLF’99
, 1999
"... This paper is devoted to a class of fuzzy orderings which play a fundamental role in decision analysis and fuzzy controlstrongly linear fuzzy (weak) orderings. First, we see that any relation of that kind can be decomposed into a crisp linear ordering and a fuzzy equivalence relation. As a conseque ..."
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Cited by 3 (3 self)
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This paper is devoted to a class of fuzzy orderings which play a fundamental role in decision analysis and fuzzy controlstrongly linear fuzzy (weak) orderings. First, we see that any relation of that kind can be decomposed into a crisp linear ordering and a fuzzy equivalence relation. As a
Slender ContextFree Languages Are Strongly Linear
"... Solving an open problem of [3], we prove that any slender contextfree language L is strongly linear, i.e., both L and its complement are linear languages. ..."
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Solving an open problem of [3], we prove that any slender contextfree language L is strongly linear, i.e., both L and its complement are linear languages.
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