.png){kind=icon}
返回
l0-norm based structural sparse least square regression for feature selection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2015.06.003.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a novel approach for feature selection with regard to the problem of structural sparse least square regression (SSLSR). Rather than employing the l(1)-norm regularization to control the sparsity, we directly work with sparse solutions via l(o)-norm regularization. In particular, we develop an effective greedy algorithm, where the forward and backward steps are combined adaptively, to resolve the SSLSR problem with the intractable l(r,o)-norm. On the one hand, features with the strongest correlation to classes are selected in the forward steps. On the other hand, redundant features which contribute little to the improvement of the objective function are removed in the backward steps. Furthermore, we provide solid theoretical analysis to prove the effectiveness of the proposed method. Experimental results on synthetic and real world data sets from different domains also demonstrate the superiority of the proposed method over the state-of-the-arts. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
Structural sparse learning
l(o)-norm
Least square regression
Feature selection
Adaptive greedy algorithm
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
分享收藏
Some sharp performance bounds for least squares regression with L1 regularization具有L1正则化的最小二乘回归的一些尖锐的性能界限.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏Graphene/Ionic Liquid Binary Electrode Material for High Performance Supercapacitor用于高性能超级电容器的石墨烯/离子液体二元电极材料
PREAI
分享收藏
查看更多内容