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Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Multiclass Sparse Discriminant Analysis Incorporating Graphical Structure among Predictors
预测变量 图形结构 多类稀疏 判别分析
2023/5/9
In the era of big data, many sparse linear discriminant analysis methods have been proposed for the classification and variable selection of the high-dimensional data. In order to solve the multiclass...
Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
Sparse graphical model Reversible Markov chain Markov equivalence class
2016/1/25
Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are said to belong to a Mar...
Vanilla Lasso for sparse classification under single index models
Vanilla Lasso sparse classification single index models
2016/1/20
This paper study sparse classification problems. We show that under single-index models, vanilla Lasso could give good estimate of unknown parameters. With this result, we see that even if the model i...
SBA-term: Sparse Bilingual Association for Terms
SBA-term Sparse Bilingual Association Terms
2016/1/19
Bilingual semantic term association is very use-ful in cross-language information retrieval, statistical machine translation, and many other applications in natural language processing. In this paper,...
For high dimensional supervised learning problems, often using problem specific assumptions can lead to greater accuracy. For problems with grouped covariates, which are believed to have sparse effect...
Laplace Inversion of Low-Resolution NMR Relaxometry Data Using Sparse Representation Methods
low-resolution NMR sparse reconstruction
2015/7/3
Low-resolution nuclear magnetic resonance (LR-NMR) relaxometry is a
powerful tool that can be harnessed for characterizing constituents in complex materials.
Cholesky-based Methods for Sparse Least Squares: The Beneˉts of Regularization
Sparse Least Squares Regularization
2015/7/3
We study the use of black-box LDL
T
factorizations for solving the augmented
systems (KKT systems) associated with least-squares problems and barrier methods
for linear programming (LP). With judi...
SOLUTION OF SPARSE LINEAR EQUATIONS USING CHOLESKY FACTORS OF AUGMENTED SYSTEMS
sparse linear equations direct methods unsymmetric matrices
2015/7/3
Cholesky factorizations have reached a high peak of e±ciency for solving sparse
symmetric systems, largely because their Analyze and Factor phases do not con°ict. We explore the
possibility of using...
SOLUTION OF SPARSE RECTANGULAR SYSTEMS USING LSQR AND CRAIG
Conjugate-gradient method least squares regularization
2015/7/3
Various methods for symmetric and unsymmetric systems are reviewed to illustrate
the parallels . We see that the extension of Craig's method closes a gap in existing
theory. However, LSQR is more ec...
Optimization algorithms typically require the solution of many systems of linear equations
Bkyk b,. When large numbers of variables or constraints are present, these linear systems could account
for...
Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis
Coordinate descent counts data overdispersion regularized likelihood sparse group penalty
2013/6/14
With the development of next generation sequencing technology, researchers have now been able to study the microbiome composition using direct sequencing, whose output are bacterial taxa counts for ea...
Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis
Coordinate descent counts data overdispersion regularized likelihood sparse group penalty
2013/6/14
With the development of next generation sequencing technology, researchers have now been able to study the microbiome composition using direct sequencing, whose output are bacterial taxa counts for ea...
Sparse approximations in spatio-temporal point-process models
latent Gaussian models linear dynamical systems log Gaussian Cox process approximate inference expectation propagation sparse inference
2013/6/14
Analysis of spatio-temporal point patterns plays an important role in several disciplines, yet inference in these systems remains computationally challenging due to the high resolution modelling gener...
Sparse Adaptive Dirichlet-Multinomial-like Processes
sparse coding adaptive parameters Dirichlet-Multinomial Polya urn data-dependent redundancy bound small/large alphabet data compression
2013/6/14
Online estimation and modelling of i.i.d. data for short sequences over large or complex "alphabets" is a ubiquitous (sub)problem in machine learning, information theory, data compression, statistical...
Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices
Covariance matrix group sparsity low-rank matrix minimax rate of convergence sparse principal component analysis principal subspace,rank detection
2013/6/14
This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minima...