搜索结果: 1-6 共查到“统计学 Structured sparsity”相关记录6条 . 查询时间(0.078 秒)
Efficient Algorithm for Extremely Large Multi-task Regression with Massive Structured Sparsity
Algorithm Large Multi-task Regression Massive Structured Sparsity
2012/9/17
We develop a highly scalable optimization method called “hierarchical group-thresholding”for solving a multi-task regression model with complex structured sparsity constraints on both input and output...
A General Framework for Structured Sparsity via Proximal Optimization
General Framework Structured Sparsity Proximal Optimization
2011/7/7
We study a generalized framework for structured sparsity. It extends the well-known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as part of a convex optimi...
Structured Sparsity via Alternating Directions Methods
structured sparsity overlapping Group Lasso alternating directions methods variable splitting augmented Lagrangian
2011/6/21
We consider a class of sparse learning problems in high dimensional feature space regularized
by a structured sparsity-inducing norm which incorporates prior knowledge of the group
structure of the ...
Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity
brain reading structured sparsity convex optimization sparse hierarchical models inter-subject validation proximal methods
2011/6/16
Inverse inference, or “brain reading”, is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data, based on pattern recognition and statistical learning. By predicting some c...
We study the problem of learning a sparse linear regression vector under additional conditions on the structure of its sparsity pattern. This problem is relevant in machine learning, statistics and s...
Learning with Structured Sparsity
Learning Structured Sparsity natural extension standard sparsity concept
2010/3/18
This paper investigates a new learning formulation called structured sparsity, which is a
natural extension of the standard sparsity concept in statistical learning and compressive sensing.By allowin...