.png){kind=icon}
返回
Unsupervised feature selection by self -paced learning regularization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2018.06.029.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Previous feature selection methods equivalently consider the samples to select important features. However, the samples are often diverse. For example, the outliers should have small or even zero weights while the important samples should have large weights. In this paper, we add a self-paced regularization in the sparse feature selection model to reduce the impact of outliers for conducting feature selection. Specifically, the proposed method automatically selects a sample subset which includes the most important samples to build an initial feature selection model, whose generalization ability is then improved by involving other important samples until a robust and generalized feature selection model has been established or all the samples have been used. Experimental results on eight real datasets show that the proposed method outperforms the comparison methods. (c) 2018 Elsevier B.V. All rights reserved.
Keyword:
Feature selection
Self-paced learning
Robust statistic
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:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
Self-taught dimensionality reduction on the high-dimensional small-sized data.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
Optimized Graph Learning Using Partial Tags and Multiple Features for Image and Video Annotation
PREAI
分享收藏A Unified Framework for High-Dimensional Analysis of M-Estimators with Decomposable RegularizersSTATISTICAL SCIENCE
OAAI
STATISTICAL SCIENCEIF3.4
OAAI分享收藏
查看更多内容