Results 1  10
of
117
Distributed Column Subset Selection on MapReduce
"... Abstract—Given a very large data set distributed over a cluster of several nodes, this paper addresses the problem of selecting a few data instances that best represent the entire data set. The solution to this problem is of a crucial importance in the big data era as it enables data analysts to und ..."
Abstract

Cited by 5 (3 self)
 Add to MetaCart
to understand the insights of the data and explore its hidden structure. The selected instances can also be used for data preprocessing tasks such as learning a lowdimensional embedding of the data points or computing a lowrank approximation of the corresponding matrix. The paper first formulates the problem
Whom You Know Matters: Venture Capital Networks and Investment Performance,
 Journal of Finance
, 2007
"... Abstract Many financial markets are characterized by strong relationships and networks, rather than arm'slength, spotmarket transactions. We examine the performance consequences of this organizational choice in the context of relationships established when VCs syndicate portfolio company inv ..."
Abstract

Cited by 138 (8 self)
 Add to MetaCart
the results reported in the following sections utilize the binary matrix, we note that all our results are robust to using network centrality measures calculated from valued matrices. 6 Unlike the undirected matrix, the directed matrix does not record a tie between VCs j and k who were members of the same
Exact Random Generation of Symmetric and Quasisymmetric Alternatingsign Matrices
, 2008
"... We show how to adapt the Monotone Coupling from the Past exact sampling algorithm to sample from some symmetric subsets of nite distributive lattices. The method is applied to generate uniform random elements of all symmetry classes of alternatingsign matrices. 1 ..."
Abstract
 Add to MetaCart
We show how to adapt the Monotone Coupling from the Past exact sampling algorithm to sample from some symmetric subsets of nite distributive lattices. The method is applied to generate uniform random elements of all symmetry classes of alternatingsign matrices. 1
Model selection: Two fundamental measures of coherence and their algorithmic significance
 in Proc. IEEE Intl. Symp. Information Theory (ISIT ’10
, 2010
"... Abstract—The problem of model selection arises in a number of contexts, such as compressed sensing, subset selection in linear regression, estimation of structures in graphical models, and signal denoising. This paper generalizes the notion of incoherence in the existing literature on model selectio ..."
Abstract

Cited by 7 (5 self)
 Add to MetaCart
Abstract—The problem of model selection arises in a number of contexts, such as compressed sensing, subset selection in linear regression, estimation of structures in graphical models, and signal denoising. This paper generalizes the notion of incoherence in the existing literature on model
1On NonBinary Constellations for ChannelCoded PhysicalLayer Network Coding
"... Abstract—We investigate channelcoded physicallayer network coding in a twoway relaying scenario, where the end nodes A and B choose their symbols, SA and SB, from a small nonbinary field, F, and adopt a nonbinary PSK modulation. The relay then directly decodes the networkcoded combination aSA ..."
Abstract
 Add to MetaCart
bound on the performance of decoding the networkcoded combinations. Simulation results show that if we adopt either i) concatenated ReedSolomon and convolutional coding or ii) lowdensity parity check codes, our nonbinary constellations can outperform the binary case significantly in the sense
ABSTRACT Title of dissertation: Weakly Compressible NavierStokes Approximation of Gas Dynamics
"... This dissertation addresses mathematical issues regarding weakly compressible approximations of gas dynamics that arise both in fluid dynamical and in kinetic settings. These approximations are derived in regimes in which (1) transport coefficients (viscosity and thermal conductivity) are small and ..."
Abstract
 Add to MetaCart
This dissertation addresses mathematical issues regarding weakly compressible approximations of gas dynamics that arise both in fluid dynamical and in kinetic settings. These approximations are derived in regimes in which (1) transport coefficients (viscosity and thermal conductivity) are small
A DEIM Induced CUR Factorization ∗
, 2014
"... We derive a CUR matrix factorization based on the Discrete Empirical Interpolation Method (DEIM). For a given matrix A, such a factorization provides a low rank approximate decomposition of the form A ≈ CUR, where C and R are subsets of the columns and rows of A, and U is constructed to make CUR a g ..."
Abstract
 Add to MetaCart
good approximation. Given a lowrank singular value decomposition A ≈ VSWT, the DEIM procedure uses V and W to select the columns and rows of A that form C and R. Through an error analysis applicable to a general class of CUR factorizations, we show that the accuracy tracks the optimal approximation
Zeta Hull Pursuits: Learning Nonconvex Data Hulls
"... Selecting a small informative subset from a given dataset, also called column sampling, has drawn much attention in machine learning. For incorporating structured data information into column sampling, research efforts were devoted to the cases where data points are fitted with clusters, simplices, ..."
Abstract
 Add to MetaCart
Selecting a small informative subset from a given dataset, also called column sampling, has drawn much attention in machine learning. For incorporating structured data information into column sampling, research efforts were devoted to the cases where data points are fitted with clusters, simplices
Efficient monitoring of endtoend network properties
 in IEEE INFOCOM
, 2005
"... Abstract — It is often desirable to monitor endtoend properties, such as loss rates or packet delays, across an entire network. However, active endtoend measurement in such settings does not scale well, and so complete networkwide measurement quickly becomes infeasible. More efficient measureme ..."
Abstract

Cited by 13 (1 self)
 Add to MetaCart
for exact recovery. We formulate a general framework for the prediction problem, propose a simple class of predictors for standard quantities of interest (e.g., averages, totals, differences), and show that linear algebraic methods of subset selection may be used to make effective choice of which paths
Results 1  10
of
117