This paper gives a rather general review of the L1 norm algorithms. The chronology and historical development of the L1 norm estimation theory for the period of 1632-1928 will be surveyed and the algorithms belonging to the after 1928 period will be categorized into three main classes of direct descent, simplex type, and other algorithms
The purpose of this study was to develop a statistical method for finding mistakes and gross errors ...
International audienceA decade ago OSCAR was introduced as a penalized estimator where the penalty t...
Necessary and sufficient conditions for minimizing an $l_1 $-norm type of objective function are der...
This paper gives a rather general review of the L1 norm algorithms. The chronology and historical de...
In this paper, three algorithms for weighted median, simple linear, and multiple m parameters L1 nor...
This paper tries to compare more accurate and efficient L1 norm regression algorithms. Other compara...
In this paper, we propose four algorithms for L1 norm computation of regression parameters, where tw...
The body of literature on classification method which estimate boundaries between the groups (classe...
1.1 A bit of background • If `2 was the norm of the 20th century, then `1 is the norm of the 21st ce...
The lasso algorithm for variable selection in linear models, introduced by Tibshirani, works by imp...
49 pages.International audienceIn numerical linear algebra, a well-established practice is to choose...
AbstractWe formalize an algorithm for solving the L1-norm best-fit hyperplane problem derived using ...
In finance, the implied volatility surface is plotted against strike price and time to maturity. Th...
Different norms are considered to replace the Euclidean norm in an algorithm given by M. H. K. Fan a...
AbstractAlgorithms for solving the nonlinear Lp (1<p<∞) problem are considered. The algorithms are a...
The purpose of this study was to develop a statistical method for finding mistakes and gross errors ...
International audienceA decade ago OSCAR was introduced as a penalized estimator where the penalty t...
Necessary and sufficient conditions for minimizing an $l_1 $-norm type of objective function are der...
This paper gives a rather general review of the L1 norm algorithms. The chronology and historical de...
In this paper, three algorithms for weighted median, simple linear, and multiple m parameters L1 nor...
This paper tries to compare more accurate and efficient L1 norm regression algorithms. Other compara...
In this paper, we propose four algorithms for L1 norm computation of regression parameters, where tw...
The body of literature on classification method which estimate boundaries between the groups (classe...
1.1 A bit of background • If `2 was the norm of the 20th century, then `1 is the norm of the 21st ce...
The lasso algorithm for variable selection in linear models, introduced by Tibshirani, works by imp...
49 pages.International audienceIn numerical linear algebra, a well-established practice is to choose...
AbstractWe formalize an algorithm for solving the L1-norm best-fit hyperplane problem derived using ...
In finance, the implied volatility surface is plotted against strike price and time to maturity. Th...
Different norms are considered to replace the Euclidean norm in an algorithm given by M. H. K. Fan a...
AbstractAlgorithms for solving the nonlinear Lp (1<p<∞) problem are considered. The algorithms are a...
The purpose of this study was to develop a statistical method for finding mistakes and gross errors ...
International audienceA decade ago OSCAR was introduced as a penalized estimator where the penalty t...
Necessary and sufficient conditions for minimizing an $l_1 $-norm type of objective function are der...
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