.png){kind=icon}
返回
A Gaussian Process Model for Data Association and a Semidefinite Programming Solution
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2014.2300701.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose a Bayesian model for the data association problem, in which trajectory smoothness is enforced through the use of Gaussian process priors. This model allows to score candidate associations using the evidence framework, thus casting the data association problem into an optimization problem. Under some additional mild assumptions, this optimization problem is shown to be equivalent to a constrained Max K-section problem. Furthermore, for K = 2, a MaxCut formulation is obtained, to which an approximate solution can be efficiently found using an SDP relaxation. Solving this MaxCut problem is equivalent to finding the optimal association out of the combinatorially many possibilities. The obtained clustering depends only on two hyperparameters, which can also be selected by maximum evidence.
Keyword:
Clustering
data association
Gaussian processes (GPs)
multitarget tracking
semidefinite programming
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
Overlapping Mixtures of Gaussian Processes for the data association problem.png)
PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏Accelerated weathering of bolted joints prepared from woven glass fiber-reinforced nanocomposites
PREAI
分享收藏分享收藏
查看更多内容