.png){kind=icon}
返回
Soft clustering for nonparametric probability density function estimation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2008.07.010.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a nonparametric probability density estimation model. The classical Parzen window approach builds a spherical Gaussian density around every input sample. Our method has a first stage where hard neighborhoods are determined for every sample. Then soft clusters are considered to merge the information coming from several hard neighborhoods. Our proposal estimates the local principal directions to yield a specific Gaussian mixture component for each Soft Cluster. This leads to Outperform other proposals where local parameter selection is not allowed and/or there are no smoothing strategies, like the manifold Parzen windows. (C) 2008 Elsevier B.V. All rights reserved.
Keyword:
Probability density estimation
Nonparametric modeling
Soft clustering
Parzen window
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
.png){kind=icon}
分享收藏
Dysfunctions of decision‐making and cognitive control as transdiagnostic mechanisms of mental disorders: advances, gaps, and needs in current research作为精神障碍的跨诊断机制的决策和认知控制功能障碍: 当前研究的进展，差距和需求
OAAI
OAAI分享收藏
查看更多内容