Studies of Joint Structure Sparsity Pursuit in the Applications of Hierarchical Variable Selection and Fused Lasso

H Jiang - 2015 - diginole.lib.fsu.edu
In this dissertation, we study joint sparsity pursuit and its applications in variable selection in
high dimensional data. The first part of dissertation focuses on hierarchical variable …

Exclusive Feature Learning on Arbitrary Structures via -norm

D Kong, R Fujimaki, J Liu, F Nie… - Advances in neural …, 2014 - proceedings.neurips.cc
Group lasso is widely used to enforce the structural sparsity, which achieves the sparsity at
inter-group level. In this paper, we propose a new formulation called``exclusive group …

Simultaneous feature selection and clustering based on square root optimization

H Jiang, S Luo, Y Dong - European Journal of Operational Research, 2021 - Elsevier
The fused least absolute shrinkage and selection operator (LASSO) simultaneously
pursuing the joint sparsity of coefficients and their successive differences has attracted …

[PDF][PDF] Logistic regression with structured sparsity

N Rao, R Nowak, CR Cox, TT Rogers - arXiv preprint arXiv:1402.4512, 2014 - Citeseer
Binary logistic regression with a sparsity constraint on the solution plays a vital role in many
high dimensional machine learning applications. In some cases, the features can be …

A unified fused Lasso approach for sparse and blocky feature selection in regression and classification

X Wu, R Liang, Z Zhang, Z Cui - arXiv preprint arXiv:2311.11068, 2023 - arxiv.org
In many applications, sparse and blocky coefficients often occur in regression and
classification problems. The fused Lasso was designed to recover these sparse structured …

Fast projections onto mixed-norm balls with applications

S Sra - Data Mining and Knowledge Discovery, 2012 - Springer
Joint sparsity offers powerful structural cues for feature selection, especially for variables that
are expected to demonstrate a “grouped” behavior. Such behavior is commonly modeled via …

Fused lasso screening rules via the monotonicity of subdifferentials

J Wang, W Fan, J Ye - IEEE transactions on pattern analysis …, 2015 - ieeexplore.ieee.org
Fused Lasso is a popular regression technique that encodes the smoothness of the data. It
has been applied successfully to many applications with a smooth feature structure …

An efficient algorithm for a class of fused lasso problems

J Liu, L Yuan, J Ye - Proceedings of the 16th ACM SIGKDD international …, 2010 - dl.acm.org
The fused Lasso penalty enforces sparsity in both the coefficients and their successive
differences, which is desirable for applications with features ordered in some meaningful …

Sparse variable selection on high dimensional heterogeneous data with tree structured responses

X Liu, H Wang, W Ye, EP Xing - arXiv preprint arXiv:1711.08265, 2017 - arxiv.org
We consider the problem of sparse variable selection on high dimension heterogeneous
data sets, which has been taken on renewed interest recently due to the growth of biological …

Hierarchical sparse modeling: A choice of two group lasso formulations

X Yan, J Bien - 2017 - projecteuclid.org
Demanding sparsity in estimated models has become a routine practice in statistics. In many
situations, we wish to require that the sparsity patterns attained honor certain problem …