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https://www.researchgate.net/publication/221617769_Phase_transitions_for_high-dimensional_joint_support_recovery
Download Citation On Jan 1, 2008, Sahand Negahban and others published Phase transitions for high-dimensional joint support recovery. Find, read and …
http://papers.nips.cc/paper/3392-phase-transitions-for-high-dimensional-joint-support-recovery.pdf
Joint support recovery under high-dimensional scaling: ... begin by providing a general set of sufficient conditions for consistent support recovery for both fixed design matrices, and random Gaussian design matrices. In addition to these basic consistency results, we then seek to ... Phase transitions for high-dimensional joint support ...
http://www.stat.uchicago.edu/~zke/files/3Phase-rev.pdf
PHASE TRANSITIONS FOR HIGH DIMENSIONAL CLUSTERING AND RELATED PROBLEMS ... low-rank matrix recovery, phase transition. 2151. 2152 J. JIN, Z. T. KE AND W. WANG We also assume that the p-dimensional data vectors Xi’s are standardized, ... Our work on support recovery is related to
https://www.pnas.org/content/116/12/5451
Mar 19, 2019 · High-dimensional generalized linear models are basic building blocks of current data analysis tools including multilayers neural networks. They arise in signal processing, statistical inference, machine learning, communication theory, and other fields. We establish rigorously the intrinsic information-theoretic limitations of inference and learning for a class of randomly generated instances ...Cited by: 57
http://www.stat.yale.edu/~snn7/papers/NegWai11_EllInfFinal.pdf
ation, group Lasso, high-dimensional inference, model selection, phase transitions, sparse approximation, subset selection. I. INTRODUCTION T HE past decade has witnessed a flurry of research on the use of -based and related convex relaxations for solving problems of high-dimensional statistical inference.Cited by: 111
https://projecteuclid.org/euclid.aos/1509436831
Supplementary Material for “Phase transitions for high dimensional clustering and related problems”. Owing to space constraints, some technical proofs and discussion are relegated a supplementary document [27]. It contains proofs of Lemmas 2.1–2.4 and 3.1–3.3, and discusses an …Cited by: 7
https://lu.seas.harvard.edu/files/yuelu/files/spca.pdf
Online Learning for Sparse PCA in High Dimensions: Exact Dynamics and Phase Transitions ... In the high-dimensional limit, the joint empirical measure of the underlying sparse eigenvector ... sparse support recovery can be obtained by examining the limiting
https://pdfs.semanticscholar.org/90a3/8b2bee60d8448fbbc61e64de7b0a17b03e12.pdf
Submitted to the Annals of Statistics PHASE TRANSITIONS FOR HIGH DIMENSIONAL CLUSTERING AND RELATED PROBLEMS By Jiashun Jin, Zheng Tracy Key, and Wanjie Wangz Carnegie Mellon University, University of Chicagoyand University of Pennsylvaniaz Consider a two-class clustering problem where we observe X
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.7164
high-dimensional scaling joint support recovery sufficient condition sharp set standard gaussian ensemble exact variable selection second set model dimension regression coefficient share linear regression problem general gaussian matrix result applies phase transition close agreement rescaled sample size support converges regularized regression ...
https://www.researchgate.net/publication/221617769_Phase_transitions_for_high-dimensional_joint_support_recovery
Download Citation On Jan 1, 2008, Sahand Negahban and others published Phase transitions for high-dimensional joint support recovery. Find, read and …
http://papers.nips.cc/paper/3392-phase-transitions-for-high-dimensional-joint-support-recovery.pdf
Joint support recovery under high-dimensional scaling: ... begin by providing a general set of sufficient conditions for consistent support recovery for both fixed design matrices, and random Gaussian design matrices. In addition to these basic consistency results, we then seek to ... Phase transitions for high-dimensional joint support ...
http://www.stat.uchicago.edu/~zke/files/3Phase-rev.pdf
PHASE TRANSITIONS FOR HIGH DIMENSIONAL CLUSTERING AND RELATED PROBLEMS BY JIASHUN JIN,ZHENG TRACY KE AND WANJIE WANG ... In the two-dimensional phase ... testing, L1-distance, lower bound, low-rank matrix recovery, phase transition. 2151. 2152 J. …
https://www.pnas.org/content/116/12/5451
Mar 19, 2019 · Optimal errors and phase transitions in high-dimensional generalized linear models. Jean Barbier, ... the information-theoretic phase transition to almost exact recovery ... The prior distribution P 0 admits a finite third moment and has at least two points in its support. h2) ...Cited by: 57
http://www.stat.yale.edu/~snn7/papers/NegWai11_EllInfFinal.pdf
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 57, NO. 6, JUNE 2011 3841 Simultaneous Support Recovery in High Dimensions: Benefits and Perils of Block -Regularization ... high-dimensional inference, model selection, phase transitions, sparse approximation, subset selection. I.Cited by: 111
https://projecteuclid.org/euclid.aos/1509436831
Supplementary Material for “Phase transitions for high dimensional clustering and related problems”. Owing to space constraints, some technical proofs and discussion are relegated a supplementary document [27]. It contains proofs of Lemmas 2.1–2.4 and 3.1–3.3, and discusses an …Cited by: 7
https://pdfs.semanticscholar.org/90a3/8b2bee60d8448fbbc61e64de7b0a17b03e12.pdf
Submitted to the Annals of Statistics PHASE TRANSITIONS FOR HIGH DIMENSIONAL CLUSTERING AND RELATED PROBLEMS By Jiashun Jin, Zheng Tracy Key, and Wanjie Wangz Carnegie Mellon University, University of Chicagoyand University of Pennsylvaniaz Consider a two-class clustering problem where we observe X
https://projecteuclid.org/euclid.aos/1509436831
Supplementary Material for “Phase transitions for high dimensional clustering and related problems”. Owing to space constraints, some technical proofs and discussion are relegated a supplementary document [27]. It contains proofs of Lemmas 2.1–2.4 and 3.1–3.3, and discusses an …
https://arxiv.org/pdf/1702.06435
Phase Transitions of Spectral Initialization for High-Dimensional Nonconvex Estimation Yue M. Lu and Gen Li Abstract—We study a spectral initialization method that serves a key role in recent work on estimating signals in nonconvex settings. Previous analysis of this method focuses on the phase retrieval problem and provides only performance ...
https://people.eecs.berkeley.edu/~wainwrig/Papers/Wai_SharpThresh.pdf
Sharp thresholds for high-dimensional and noisy sparsity recovery using ℓ1-constrained quadratic programmming (Lasso) Martin J. Wainwright1 Abstract—The problem of consistently estimating the sparsity pattern of a vector β∗ ∈ Rp based on observations contaminated by noise arises in various contexts, including signal denoising,
http://www.stat.yale.edu/~snn7/papers/NegWai11_EllInfFinal.pdf
ation, group Lasso, high-dimensional inference, model selection, phase transitions, sparse approximation, subset selection. I. INTRODUCTION T HE past decade has witnessed a flurry of research on the use of -based and related convex relaxations for solving problems of high-dimensional statistical inference.
https://www.researchgate.net/publication/313879524_Phase_Transitions_of_Spectral_Initialization_for_High-Dimensional_Nonconvex_Estimation
Phase Transitions of Spectral Initialization for High-Dimensional Nonconvex Estimation Article · February 2017 with 10 Reads How we measure 'reads'
https://core.ac.uk/display/73369771
Online Learning for Sparse PCA in High Dimensions: Exact Dynamics and Phase Transitions . ... In the high-dimensional limit, the joint empirical measure of the underlying sparse eigenvector and its estimate provided by the algorithm is shown to converge weakly to a deterministic, measure-valued process. ... Exact Dynamics and Phase Transitions .
https://core.ac.uk/display/94038081
Online Learning for Sparse PCA in High Dimensions: Exact Dynamics and Phase Transitions . ... In the high-dimensional limit, the joint empirical measure of the underlying sparse eigenvector and its estimate provided by the algorithm is shown to converge weakly to a deterministic, measure-valued process. ... A steady-state analysis of the ...
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2865881/
Nov 13, 2009 · Finally, in a ‘joint’ approach, R(X) ... Tanner J. Inpress Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing. ... High dimensional statistical inference and random matrices. Int. …
http://www-personal.umich.edu/~gaozheng/research/papers/FLESR_arXiv.pdf
We study the support recovery problem for a high-dimensional signal observed with additive noise. With suitable parametrization of the signal sparsity and magnitude of its non-zero components, we characterize a phase-transition phenomenon akin to the signal de-tection problem studied by Ingster in 1998. Speci cally, if the signal
http://adsabs.harvard.edu/abs/2016arXiv160902191W
Online Learning for Sparse PCA in High Dimensions: Exact Dynamics and Phase Transitions: Authors: Wang ... In the high-dimensional limit, the joint empirical measure of the underlying sparse eigenvector and its estimate provided by the algorithm is shown to converge weakly to a deterministic, measure-valued process. ... performance metrics such ...
https://www.groundai.com/project/phase-transitions-of-spectral-initialization-for-high-dimensional-nonconvex-estimation/2
Our analysis also reveals a phase transition phenomenon that depends on the ratio between the number of samples and the signal dimension. ... Phase Transitions of Spectral Initialization for High-Dimensional Nonconvex Estimation ... [1, 2], phase retrieval , signal recovery from quantized measurements , and various single-index and generalized ...
http://statistics.berkeley.edu/sites/default/files/tech-reports/774.pdf
Simultaneous support recovery in high dimensions: Benefits and perils of block ... problem class, we prove that the ℓ1/ℓ∞-regularized method undergoes a phase transition ... high-dimensional consistency (or inconsistency) of various types of estimators.
https://projecteuclid.org/euclid.aos/1382547512
Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions ... Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions. ... Two-sample covariance matrix testing and support recovery in high-dimensional and sparse settings. J. Amer. Statist. Assoc. 108 265 ...
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