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An objectoriented framework for robust multivariate analysis
 Journal of Statistical Software
"... Taking advantage of the S4 class system of the programming environment R, which facilitates the creation and maintenance of reusable and modular components, an objectoriented framework for robust multivariate analysis was developed. The framework resides in the packages robustbase and rrcov and incl ..."
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Cited by 14 (1 self)
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Taking advantage of the S4 class system of the programming environment R, which facilitates the creation and maintenance of reusable and modular components, an objectoriented framework for robust multivariate analysis was developed. The framework resides in the packages robustbase and rrcov
An Object Oriented Framework for Robust Multivariate Analysis
"... This introduction to the R package rrcov is a (slightly) modified version of Todorov and Filzmoser (2009), published in the Journal of Statistical Software. Taking advantage of the S4 class system of the programming environment R, which facilitates the creation and maintenance of reusable and modula ..."
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Cited by 6 (0 self)
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and modular components, an object oriented framework for robust multivariate analysis was developed. The framework resides in the packages robustbase and rrcov and includes an almost complete set of algorithms for computing robust multivariate location and scatter, various robust methods for principal
1Computational Connections Between Robust Multivariate Analysis and Clustering
"... In this paper we examine some of the relationships between two important optimization problems that arise in statistics: robust estimation of multivariate location and shape parameters and maximum likelihood assignment of multivariate data to clusters. We offer a synthesis and generalization of ..."
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In this paper we examine some of the relationships between two important optimization problems that arise in statistics: robust estimation of multivariate location and shape parameters and maximum likelihood assignment of multivariate data to clusters. We offer a synthesis and generalization of
Multivariable Feedback Control: Analysis
 span (B∗) und Basis B∗ = { ω1
, 2005
"... multiinput, multioutput feedback control design for linear systems using the paradigms, theory, and tools of robust control that have arisen during the past two decades. The book is aimed at graduate students and practicing engineers who have a basic knowledge of classical control design and st ..."
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Cited by 526 (24 self)
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multiinput, multioutput feedback control design for linear systems using the paradigms, theory, and tools of robust control that have arisen during the past two decades. The book is aimed at graduate students and practicing engineers who have a basic knowledge of classical control design
Robust Principal Component Analysis?
, 2009
"... This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a lowrank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the lowrank and the sparse co ..."
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Cited by 542 (26 self)
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components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the ℓ1 norm. This suggests the possibility of a principled approach to robust principal component analysis
Discrete Multivariate Analysis: Theory and Practice
, 1975
"... the collaboration of Richard J. Light and Frederick Mosteller. ..."
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Cited by 818 (47 self)
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the collaboration of Richard J. Light and Frederick Mosteller.
Some methods for classification and analysis of multivariate observations
 In 5th Berkeley Symposium on Mathematical Statistics and Probability
, 1967
"... The main purpose of this paper is to describe a process for partitioning an Ndimensional population into k sets on the basis of a sample. The process, which is called 'kmeans, ' appears to give partitions which are reasonably ..."
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Cited by 2978 (3 self)
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The main purpose of this paper is to describe a process for partitioning an Ndimensional population into k sets on the basis of a sample. The process, which is called 'kmeans, ' appears to give partitions which are reasonably
Mean shift: A robust approach toward feature space analysis
 In PAMI
, 2002
"... A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data the convergence ..."
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Cited by 2357 (37 self)
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A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data
Fast and robust fixedpoint algorithms for independent component analysis
 IEEE TRANS. NEURAL NETW
, 1999
"... Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s informat ..."
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Cited by 857 (33 self)
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Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s
Survey on Independent Component Analysis
 NEURAL COMPUTING SURVEYS
, 1999
"... A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the ..."
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Cited by 2239 (103 self)
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A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation
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