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753
Lanczos Vectors versus Singular Vectors for Effective Dimension Reduction
, 2008
"... This paper takes an indepth look at a technique for computing filtered matrixvector (matvec) products which are required in many data analysis applications. In these applications the data matrix is multiplied by a vector and we wish to perform this product accurately in the space spanned by a few ..."
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Cited by 6 (2 self)
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. This advantage comes without sacrificing accuracy. The effectiveness of this approach is demonstrated on a few sample applications requiring dimension reduction, including information retrieval and face recognition. The proposed technique can be applied as a replacement to the truncated SVD technique whenever
Explicit Substitutions and Reducibility
 Journal of Logic and Computation
, 2001
"... . We consider reducibility sets dened not by induction on types but by induction on sequents as a tool to prove strong normalization of systems with explicit substitution. To illustrate this point, we give a proof of strong normalization (SN) for simplytyped callbyname ~calculus enriched with op ..."
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Cited by 8 (1 self)
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calculi too) and as an application we derive the strong normalization of Parigot's simplytyped calculus with explicit substitution. Introduction Explicit substitution in calculus The traditional theory of calculus relies on reduction, that is the capture by a function of its argument followed
On Local Intrinsic Dimension Estimation and Its Applications
"... Abstract—In this paper, we present multiple novel applications for local intrinsic dimension estimation. There has been much work done on estimating the global dimension of a data set, typically for the purposes of dimensionality reduction. We show that by estimating dimension locally, we are able t ..."
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Cited by 7 (1 self)
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Abstract—In this paper, we present multiple novel applications for local intrinsic dimension estimation. There has been much work done on estimating the global dimension of a data set, typically for the purposes of dimensionality reduction. We show that by estimating dimension locally, we are able
Reducing False Sharing on Shared Memory Multiprocessors through Compile Time Data Transformations.
 In Proceedings of the Fifth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
, 1994
"... We have developed compiler algorithms that analyze coarsegrained, explicitly parallel programs and restructure their shared data to minimize the number of false sharing misses. The algorithms analyze the perprocess data accesses to shared data, use this information to pinpoint the data structures ..."
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Cited by 127 (1 self)
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that are prone to false sharing and choose an appropriate transformation to reduce it. The algorithms eliminated an average (across the entire workload) of 64% of false sharing misses, and in two programs more than 90%. However, how well the reduction in false sharing misses translated into improved execution
Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections
, 2007
"... For1 dimension reduction in the l1 norm, the method of Cauchy random projections multiplies the original data matrix A ∈ Rn×D with a random matrix R ∈ RD×k (k ≪ D) whose entries are i.i.d. samples of the standard Cauchy C(0,1). Because of the impossibility result, one can not hope to recover the pai ..."
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Cited by 29 (0 self)
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and the geometric mean estimators are about 80 % efficient compared to the MLE. We analyze the moments of the MLE and propose approximating its distribution of by an inverse Gaussian. Keywords: dimension reduction, l1 norm, JohnsonLindenstrauss (JL) lemma, Cauchy random projections
Explicit substitutions for the calculus
"... Abstract. The calculus is a calculus with a controllike operator whose reduction rules are closely related to normalisation procedures in classical logic. We introduce exp, an explicit substitution calculus for , and study its properties. In particular, we show that exp preserves strong normalisa ..."
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Abstract. The calculus is a calculus with a controllike operator whose reduction rules are closely related to normalisation procedures in classical logic. We introduce exp, an explicit substitution calculus for , and study its properties. In particular, we show that exp preserves strong
Approximating conditional distribution functions using dimension reduction
 Ann.Statist
, 2005
"... Motivated by applications to prediction and forecasting, we suggest methods for approximating the conditional distribution function of a random variable Y given a dependent random dvector X. The idea is to estimate not the distribution of Y X, but that of Y θ T X, where the unit vector θ is selec ..."
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Cited by 9 (0 self)
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Motivated by applications to prediction and forecasting, we suggest methods for approximating the conditional distribution function of a random variable Y given a dependent random dvector X. The idea is to estimate not the distribution of Y X, but that of Y θ T X, where the unit vector θ
On Partial Sufficient Dimension Reduction With Applications to Partially Linear MultiIndex Models
"... Partial dimension reduction is a general method to seek informative convex combinations of predictors of primary interest, which includes dimension reduction as its special case when the predictors in the remaining part are constants. In this article, we propose a novel method to conduct partial di ..."
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Partial dimension reduction is a general method to seek informative convex combinations of predictors of primary interest, which includes dimension reduction as its special case when the predictors in the remaining part are constants. In this article, we propose a novel method to conduct partial
Extended Lanczos Bidiagonalization for Dimension Reduction in Information Retrieval
"... We describe an extended bidiagonalization scheme designed to compute lowrank approximations of very large data matrices. Its goal is identical to that of the truncated singular value decomposition, but it is significantly cheaper. It consists in an extension of the standard Lanczos bidiagonalizati ..."
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We describe an extended bidiagonalization scheme designed to compute lowrank approximations of very large data matrices. Its goal is identical to that of the truncated singular value decomposition, but it is significantly cheaper. It consists in an extension of the standard Lanczos
Simplified Control for Complex IT Systems Using Dimension Reduction
"... Automated management of complex information technology (IT) applications and systems require dynamic configuration of both applicationlevel and systemlevel parameters. The existence of large number of tunable parameters makes it difficult to design a feedback controller that adjusts these paramete ..."
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these parameters effectively in order to achieve applicationlevel quality of service (QoS) targets. In this paper, we introduce a new approach for simplified control of complex IT systems based on dimension reduction techniques. It combines online selection of critical control knobs through Lasso — a powerful L1
Results 11  20
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753