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Analysis of HighDimensional Signal Data by Manifold Learning and Convolutions
"... A novel concept for the analysis of highdimensional signal data is proposed. To this end, customized techniques from manifold learning are combined with convolution transforms, being based on wavelets. The utility of the resulting method is supported by numerical examples concerning lowdimensional ..."
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Cited by 4 (4 self)
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A novel concept for the analysis of highdimensional signal data is proposed. To this end, customized techniques from manifold learning are combined with convolution transforms, being based on wavelets. The utility of the resulting method is supported by numerical examples concerning lowdimensional
A High Dimensional Signal Space Implementation of FDTS/DF
 IEEE Trans. Magn
, 1996
"... A procedure for designing FDTS/DF as a high dimensional signal space detector is presented. The procedure is applied to the three dimensional case to illustrate the resulting detector structure. An equalization and code constraint reduce the number of boundaries and to eliminate the multipliers from ..."
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Cited by 10 (7 self)
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A procedure for designing FDTS/DF as a high dimensional signal space detector is presented. The procedure is applied to the three dimensional case to illustrate the resulting detector structure. An equalization and code constraint reduce the number of boundaries and to eliminate the multipliers
EigenPrism: Inference for HighDimensional SignaltoNoise Ratios
"... Abstract Consider the following three important problems in statistical inference, namely, constructing confidence intervals for (1) the error of a highdimensional (p > n) regression estimator, (2) the linear regression noise level, and (3) the genetic signaltonoise ratio of a continuousvalu ..."
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Abstract Consider the following three important problems in statistical inference, namely, constructing confidence intervals for (1) the error of a highdimensional (p > n) regression estimator, (2) the linear regression noise level, and (3) the genetic signaltonoise ratio of a continuous
FAST COMPRESSIVE SENSING OF HIGHDIMENSIONAL SIGNALS WITH TREESTRUCTURE SPARSITY PATTERN
"... Compressive sensing of multidimensional signals (tensors) only receives limited attention. Separable sensing and proper sparsity pattern play two key roles for compressive sensing of tensors to be feasible and efficient. In view of inherent characteristic of 2D images and 3D videos, we propose the ..."
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Compressive sensing of multidimensional signals (tensors) only receives limited attention. Separable sensing and proper sparsity pattern play two key roles for compressive sensing of tensors to be feasible and efficient. In view of inherent characteristic of 2D images and 3D videos, we propose
Sample eigenvalue based detection of highdimensional signals in white noise using relatively few samples
, 2007
"... ..."
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
, 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
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Cited by 1513 (20 self)
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Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear
Boosting for highdimensional linear models
 THE ANNALS OF STATISTICS
, 2006
"... We prove that boosting with the squared error loss, L2Boosting, is consistent for very highdimensional linear models, where the number of predictor variables is allowed to grow essentially as fast as O(exp(sample size)), assuming that the true underlying regression function is sparse in terms of th ..."
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Cited by 80 (4 self)
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We prove that boosting with the squared error loss, L2Boosting, is consistent for very highdimensional linear models, where the number of predictor variables is allowed to grow essentially as fast as O(exp(sample size)), assuming that the true underlying regression function is sparse in terms
Variants of Clustering
, 2013
"... ”We are drowning in information, but starving for knowledge. ” [John Naisbett] The objective of exploratory data analysis is to produce simplified descriptions and summaries of large data sets. Clustering: Discover similarity relations between data objects in highdimensional signal space. Self Orga ..."
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”We are drowning in information, but starving for knowledge. ” [John Naisbett] The objective of exploratory data analysis is to produce simplified descriptions and summaries of large data sets. Clustering: Discover similarity relations between data objects in highdimensional signal space. Self
Systematic design of unitary spacetime constellations
 IEEE TRANS. INFORM. THEORY
, 2000
"... We propose a systematic method for creating constellations of unitary space–time signals for multipleantenna communication links. Unitary space–time signals, which are orthonormal in time across the antennas, have been shown to be welltailored to a Rayleigh fading channel where neither the transm ..."
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Cited by 201 (10 self)
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the familiar maximumEuclideandistance norm. Our construction begins with the first signal in the constellation—an oblong complexvalued matrix whose columns are orthonormal—and systematically produces the remaining signals by successively rotating this signal in a highdimensional complex space
Highdimensional Clustering Problems
, 904
"... Clustering is one of the most widely used procedures in the analysis of microarray data, for example with the goal of discovering cancer subtypes based on observed heterogeneity of genetic marks between different tissues. It is wellknown that in such highdimensional settings, the existence of many ..."
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noise variables can overwhelm the few signals embedded in the highdimensional space. We propose a novel Bayesian approach based on Dirichlet process with a sparsity prior that simultaneous performs variable selection and clustering, and also discover variables that only distinguish a subset
Results 1  10
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2,516