.png){kind=icon}
返回
Multi-target feature selection with subspace learning and manifold regularization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2024.127533.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Existing supervised Multi -Target Feature Selection (MTFS) methods seldom consider the nearest -neighbor relationship and statistical correlation of samples underlying the output space, which leads the result of feature selection to be easily interfered by the output noise, thus making it difficult to achieve satisfactory performance. This paper proposes a novel MTFS method to preserve both the global and local target correlations. Specifically, the low -rank constraint is introduced to achieve multi -layer regression structure to better decouple the inter -input and inter -target relationships. Moreover, the local nearest -neighbor relationships and variable correlations of the sample points in the output space are also explored through adaptive graph and manifold learning, to better utilize the target correlations to improve the MTFS performance. Following the above principle, the resulting objective function and the corresponding optimization algorithm are proposed. Extensive experiments on several public datasets show that the proposed method is superior to other state-of-the-art methods.
Keyword:
Multi-target regression
Subspace learning
Feature selection
Adaptive graph learning
Manifold framework
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
分享收藏Multi-target regression via input space expansion: treating targets as inputs.png)
MACHINE LEARNING
OAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏
分享收藏分享收藏
查看更多内容