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首页> 外文期刊>Journal of applied statistics >Bayesian inference over ICA models: application to multibiometric score fusion with quality estimates
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Bayesian inference over ICA models: application to multibiometric score fusion with quality estimates

机译：基于ICA模型的贝叶斯推断：在具有质量估计的多生物评分融合中的应用

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摘要

Bayesian networks are not well-formulated for continuous variables. The majority of recent works dealing with Bayesian inference are restricted only to special types of continuous variables such as the conditional linear Gaussian model for Gaussian variables. In this context, an exact Bayesian inference algorithm for clusters of continuous variables which may be approximated by independent component analysis models is proposed. The complexity in memory space is linear and the overfitting problem is attenuated, while the inference time is still exponential. Experiments for multibiometric score fusion with quality estimates are conducted, and it is observed that the performances are satisfactory compared to some known fusion techniques.
机译：对于连续变量，贝叶斯网络的格式不正确。最近有关贝叶斯推断的大多数著作都只限于特殊类型的连续变量，例如针对高斯变量的条件线性高斯模型。在这种情况下，针对连续变量的聚类提出了一种精确的贝叶斯推理算法，该算法可以通过独立的成分分析模型来近似。存储空间的复杂度是线性的，过拟合问题得到了缓解，而推理时间仍然是指数级的。进行了具有质量估计值的多生物评分融合的实验，并且观察到与某些已知融合技术相比，性能令人满意。

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