.png){kind=icon}
返回
Large Margin Subspace Learning for feature selection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2013.02.012.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recent research has shown the benefits of large margin framework for feature selection. In this paper, we propose a novel feature selection algorithm, termed as Large Margin Subspace Learning (LMSL), which seeks a projection matrix to maximize the margin of a given sample, defined as the distance between the nearest missing (the nearest neighbor with the different label) and the nearest hit (the nearest neighbor with the same label) of the given sample. Instead of calculating the nearest neighbor of the given sample directly, we treat each sample with different (same) labels with the given sample as a potential nearest missing (hint), with the probability estimated by kernel density estimation. By this way, the nearest missing (hint) is calculated as an expectation of all different (same) class samples. In order to perform feature selection, an l(2,1)-norm is imposed on the projection matrix to enforce row-sparsity. An efficient algorithm is then proposed to solve the resultant optimization problem. Comprehensive experiments are conducted to compare the performance of the proposed algorithm with the other five state-of-the-art algorithms RFS, SPFS, mRMR, TR and LLFS, it achieves better performance than the former four. Compared with the algorithm LLFS, the proposed algorithm has a competitive performance with however a significantly faster computational. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Feature selection
l(2,1)-norm regularization
Large margin maximization
Subspace learning
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
Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis基于混合核典型相关分析的降维方法.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Self-taught dimensionality reduction on the high-dimensional small-sized dataPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏分享收藏
查看更多内容