Results 1  10
of
127,528
Robustness of Reweighted Least Squares Kernel Based Regression
, 2009
"... 1 Kernel Based Regression (KBR) minimizes a convex risk over a possibly infinite dimensional reproducing kernel Hilbert space. Recently it was shown that KBR with a least squares loss function may have some undesirable properties from a robustness point of view: even very small amounts of outliers c ..."
Abstract
 Add to MetaCart
theoretical framework for reweighted Least Squares KBR (LSKBR) and analyze its robustness. Some important differences are found with respect to linear regression, indicating that LSKBR with a bounded kernel is much more suited for reweighting. In two special cases our results can be translated
ROBUST REGRESSION COMPUTATION USING ITERATIVELY REWEIGHTED LEAST SQUARES*
"... Abstract. Several variants of Newton’s method are used to obtain estimates of solution vectors and residual vectors for the linear model Ax b / e btrue using an iteratively reweighted least squares criterion, which tends to diminish the influence of outliers compared with the standard least squares ..."
Abstract
 Add to MetaCart
for sparse wellconditioned and illconditioned problems. Key words, iteratively reweighted least squares, robust regression AMS(MOS) subject classifications. 62J05, 65F20 1. Introduction. Consider
Iteratively reweighted least . . .
, 2009
"... Under certain conditions (known as the restricted isometry property, or RIP) on the m N matrix ˆ (where m<N), vectors x 2 RN that are sparse (i.e., have most of their entries equal to 0) can be recovered exactly from y WD ˆx even though ˆ 1.y / is typically an.N m/–dimensional hyperplane; in addi ..."
Abstract
 Add to MetaCart
; in addition, x is then equal to the element in ˆ 1.y / of minimal `1norm. This minimal element can be identified via linear programming algorithms. We study an alternative method of determining x, as the limit of an iteratively reweighted least squares (IRLS) algorithm. The main step of this IRLS finds
Iteratively Reweighted
"... and he has a seat on the board of directors of João Pinheiro Foundation Quality of human capital seems to be an extremely important feature to be disregarded in the evaluation of this factor impacts on income per worker (rate of growth and level). This is the reason for the emergence of many recent ..."
Abstract
 Add to MetaCart
the human capital direct impacts on Brazilian States output level in the years 1970, 1980, 1991, and 2000. The methods employed are Ordinary Least Squares (OLS), Iteratively Reweighted Least Squares (IRLS) and Panel Data regressions.
Multiple Task Learning Using Iteratively Reweighted Least Square
 PROCEEDINGS OF THE TWENTYTHIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2013
"... Multiple task learning (MTL) is becoming popular due to its theoretical advances and empirical successes. The key idea of MTL is to explore the hidden relationships among multiple tasks to enhance learning performance. Recently, many MTL algorithms have been developed and applied to various problems ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
and formulate the objective as an unconstrained convex problem. To derive the optimal solution efficiently, we propose to use an Iteratively Reweighted Least Square (IRLS) method with a preconditioned conjugate gradient, which is computationally affordable for high dimensional data. Extensive experiments
Poisson Iteratively Reweighted Least Squares (PIRLS)
"... • Poisson regression models are commonly used for modeling risk factors and other covariates when the rates of disease are small, i.e., cancer • An area of research in which Poisson regression of cancer rates is commonly employed is the investigation of radiationinduced cancer among ..."
Abstract
 Add to MetaCart
• Poisson regression models are commonly used for modeling risk factors and other covariates when the rates of disease are small, i.e., cancer • An area of research in which Poisson regression of cancer rates is commonly employed is the investigation of radiationinduced cancer among
Robust Reweighted MAP Motion Estimation
 IEEE Trans. on PAMI
, 1998
"... This paper proposes a motion estimation algorithm that is robust to motion discontinuity and noise. The proposed algorithm is constructed by embedding the least median squares (LMedS) of robust statistics into the maximum a posteriori (MAP) estimator. Difficulties in accurate estimation of the motio ..."
Abstract

Cited by 8 (0 self)
 Add to MetaCart
of the motion field arise from the smoothness constraint and the sensitivity to noise. To cope robustly with these problems, a median operator and the concept of reweighted least squares (RLS) are applied to the MAP motion estimator, resulting in the reweighted robust MAP (RRMAP).The proposed RRMAP motion
ADAPTIVE ITERATIVE REWEIGHTED LEAST SQUARES DESIGN OF ¢ ¡ FIR FILTERS
"... This paper presents an efficient adaptive algorithm for designing FIR digital filters that are efficient according to an £¥ ¤ error criteria. The algorithm is an extension of Burrus ’ iterative reweighted leastsquares (IRLS) method for approximating £¥ ¤ filters. Such algorithm will converge for mo ..."
Abstract
 Add to MetaCart
This paper presents an efficient adaptive algorithm for designing FIR digital filters that are efficient according to an £¥ ¤ error criteria. The algorithm is an extension of Burrus ’ iterative reweighted leastsquares (IRLS) method for approximating £¥ ¤ filters. Such algorithm will converge
2D ITERATIVELY REWEIGHTED LEAST SQUARES LATTICE ALGORITHM AND ITS APPLICATION TO DEFECT DETECTION IN TEXTURED IMAGES*
"... Abstract In this paper, a 2D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares ..."
Abstract
 Add to MetaCart
Abstract In this paper, a 2D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares
Results 1  10
of
127,528