Gradient LASSO for feature selection

Authors:
Yongdai Kim;Jinseog Kim
Affiliations:
Seoul National University, Seoul, Korea;Seoul National University, Seoul, Korea
Venue:
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Year:
2004

Citing 5
Cited 6

A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Atomic Decomposition by Basis Pursuit

SIAM Journal on Scientific Computing
Outcomes of the equivalence of adaptive ridge with least absolute shrinkage

Proceedings of the 1998 conference on Advances in neural information processing systems II
Structural Modelling with Sparse Kernels

Machine Learning
Grafting: fast, incremental feature selection by gradient descent in function space

The Journal of Machine Learning Research

Shrinkage and model selection with correlated variables via weighted fusion

Computational Statistics & Data Analysis
Feature selection for support vector regression using probabilistic prediction

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification

The Journal of Machine Learning Research
A Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data

Journal of Computational Neuroscience
Robust feature selection based on regularized brownboost loss

Knowledge-Based Systems
Variable selection for generalized linear mixed models by L1-penalized estimation

Statistics and Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the shrinkage and variable selection simultaneously. Since LASSO uses the L1 penalty, the optimization should rely on the quadratic program (QP) or general non-linear program which is known to be computational intensive. In this paper, we propose a gradient descent algorithm for LASSO. Even though the final result is slightly less accurate, the proposed algorithm is computationally simpler than QP or non-linear program, and so can be applied to large size problems. We provide the convergence rate of the algorithm, and illustrate it with simulated models as well as real data sets.