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期刊论文 [9]
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2017 [12]
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Symmetric and Nonnegative Latent Factor Models for Undirected, High-Dimensional, and Sparse Networks in Industrial Applications
期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2017, 卷号: 13, 期号: 6, 页码: 3098-3107
作者:
Luo, Xin
;
Sun, Jianpei
;
Wang, Zidong
;
Li, Shuai
;
Shang, Mingsheng
收藏
  |  
浏览/下载:53/0
  |  
提交时间:2018/03/05
Big data application
high-dimensional, and sparse (SHiDS) matrix
nonnegative latent factor (NLF) model
symmetry
undirected HiDS network
Graph Regularized Non-Negative Low-Rank Matrix Factorization for Image Clustering
期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2017, 卷号: 47, 期号: 11, 页码: 3840-3853
作者:
Li, Xuelong
;
Cui, Guosheng
;
Dong, Yongsheng
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  |  
浏览/下载:42/0
  |  
提交时间:2017/12/25
Data Representation
Graph Regularization
Image Clustering
Low-rank Recovery
Non-negative Matrix Factorization (Nmf)
ORACLE INEQUALITIES AND SELECTION CONSISTENCY FOR WEIGHTED LASSO IN HIGH-DIMENSIONAL ADDITIVE HAZARDS MODEL
期刊论文
STATISTICA SINICA, 2017, 卷号: 27, 期号: 4, 页码: 1903-1920
作者:
Zhang, Haixiang
;
Sun, Liuquan
;
Zhou, Yong
;
Huang, Jian
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  |  
浏览/下载:17/0
  |  
提交时间:2018/07/30
High-dimensional covariates
oracle inequalities
sign consistency
survival analysis
variable selection
ORACLE INEQUALITIES AND SELECTION CONSISTENCY FOR WEIGHTED LASSO IN HIGH-DIMENSIONAL ADDITIVE HAZARDS MODEL
期刊论文
STATISTICA SINICA, 2017, 卷号: 27, 期号: 4, 页码: 1903-1920
作者:
Zhang, Haixiang
;
Sun, Liuquan
;
Zhou, Yong
;
Huang, Jian
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2019/08/22
High-dimensional covariates
oracle inequalities
sign consistency
survival analysis
variable selection
Feature Selection Based on Structured Sparsity: A Comprehensive Study
期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 卷号: 28, 期号: 7, 页码: 1490-1507
作者:
Gui, Jie
;
Sun, Zhenan
;
Ji, Shuiwang
;
Tao, Dacheng
;
Tan, Tieniu
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  |  
浏览/下载:21/0
  |  
提交时间:2017/09/12
Dimensionality Reduction
Feature Selection
Sparse
Structured Sparsity
Feature Selection Based on Structured Sparsity: A Comprehensive Study
期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 卷号: 28, 期号: 7, 页码: 1490-1507
作者:
Gui, Jie
;
Sun, Zhenan
;
Ji, Shuiwang
;
Tao, Dacheng
;
Tan, Tieniu
收藏
  |  
浏览/下载:24/0
  |  
提交时间:2018/06/04
Dimensionality Reduction
Feature Selection
Sparse
Structured Sparsity
2D normalized iterative hard thresholding algorithm for fast compressive radar imaging
期刊论文
Remote Sensing, 2017, 卷号: 9, 期号: 6, 页码: 1-16
作者:
Yang WG(杨文广)
;
Yang J(杨佳)
;
Li GX(李恭新)
;
Liu LQ(刘连庆)
;
Wang WX(王文学)
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  |  
浏览/下载:33/0
  |  
提交时间:2017/07/17
fast compressive radar imaging
compressive sensing
two dimensional normalized iterative hard thresholding (2D-NIHT) algorithm
compressive radar imaging model
reconstruction performance
Confidence intervals for sparse precision matrix estimation via Lasso penalized D-trace loss
期刊论文
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2017, 卷号: 46, 期号: 24, 页码: 12299-12316
作者:
Huang Xudong
;
Li Mengmeng
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2019/08/22
Confidence intervals
D-trace loss
High-dimensional
Precision matrix
Sparsity
L1 least squares for sparse high-dimensional LDA
其他
2017-01-01
Li, Yanfang
;
Jia, Jinzhu
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2017/12/03
High-dimensional LDA
Lasso
sparsity
LINEAR DISCRIMINANT-ANALYSIS
LASSO
CLASSIFICATION
REGRESSION
Efficient extraction of non-negative latent factors from high-dimensional and sparse matrices in industrial applications
会议论文
Barcelona, Catalonia, Spain, December 12, 2016 - December 15, 2016
作者:
Luo, Xin
;
Shang, Mingsheng
;
Li, Shuai
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  |  
浏览/下载:10/0
  |  
提交时间:2018/03/16
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