.png){kind=icon}
返回
Discriminant component analysis via distance correlation maximization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2019.107052.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the following study, an innovative supervised dimensionality reduction technique is proposed. dCor-based Dimensionality Reduction or dDR technique is based on distance correlation; a powerful correlation measure which is applicable to arbitrary-dimensional random variables. By projecting the samples to a lower dimensional space, dDR maximizes the correlation between explanatory and response variables. The proposed dDR algorithm can be easily implemented and it is computationally efficient. Moreover, it has a closed-form and a simple solution which makes it significantly effective in many different applications. In order to apply the proposed technique on non-linear problems, the kernel version of the dDR is also derived. Extensive analyses and empirical experiments across various visualization, classification, and regression tasks indicate that our algorithm is the method of choice; as it offers statistically superior results in comparison with other state-of-the-art approaches in the literature. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Dimensionality reduction
Distance correlation (dCor)
Kernel methods
Classification
Regression
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
.png){kind=icon}
分享收藏分享收藏分享收藏分享收藏
查看更多内容