.png){kind=icon}
返回
Sequential Independent Component Analysis Density Estimation
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2018.2791358.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A problem of multivariate probability density function estimation by exploiting linear independent components analysis (ICA) is addressed. Historically, ICA density estimation was initially proposed under the name projection pursuit density estimation (PPDE) and two basic methods, named forward and backward, were published. We derive a modification of the forward PPDE method, which avoids a computationally demanding optimization involving Monte Carlo sampling of the original method. The results of the experiments show that the proposed method presents an attractive choice for density estimation, which is pronounced for a small number of training observations. Under such conditions, our method usually outperforms model-based Gaussian mixture model. We also found that our method obtained better results than the backward PPDE methods in the situation of nonfactorizable underlying density functions. The proposed method has demonstrated a competitive performance compared with the support vector machine and the extreme learning machine in some real classification tasks.
Keyword:
Gaussian mixture model (GMM)
independent component analysis (ICA)
multivariate probability density estimation
projection pursuit (PP)
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:
8.9
论文数:
7.5K
被引数:
7.2W
机构
引用论文
Productivity enhancement of solar still by PCM and Nanoparticles miscellaneous basin absorbing materials.png)
Desalination
OAAI
DesalinationIF0
OAAI分享收藏
查看更多内容