Learning the geometry of common latent variables using alternating-diffusion
Learning the geometry of common latent variables using alternating-diffusion
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DOI：
10.1016/j.acha.2015.09.002
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发表时间：
2018-05-01
影响因子：
2.5
通讯作者：
Talmon, Ronen
中科院分区：
文献类型：
--
作者：
Lederman, Roy R.;Talmon, Ronen
数据分析中的一个挑战是区分数据中表现出的不同可变性来源。在本文中，我们考虑的情况下，多个传感器测量相同的物理现象，这样的物理现象的属性表现为一个隐藏的共同来源的变化（我们想提取），而每个传感器有自己的传感器特定的影响（隐变量，我们想抑制）;测量和隐变量之间的关系是未知的。我们提出了一个数据驱动的方法的基础上交替产品的扩散算子，并表明它提取的共同来源的变异性。此外，我们表明，它提取的共同来源的可变性在多传感器实验，如果它是一个标准的流形学习算法用于分析一个简单的单传感器实验，其中的共同来源的可变性是唯一的来源的可变性。(c)2015爱思唯尔公司All rights reserved.
One of the challenges in data analysis is to distinguish between different sources of variability manifested in data. In this paper, we consider the case of multiple sensors measuring the same physical phenomenon, such that the properties of the physical phenomenon are manifested as a hidden common source of variability (which we would like to extract), while each sensor has its own sensor-specific effects (hidden variables which we would like to suppress); the relations between the measurements and the hidden variables are unknown. We present a data-driven method based on alternating products of diffusion operators and show that it extracts the common source of variability. Moreover, we show that it extracts the common source of variability in a multi-sensor experiment as if it were a standard manifold learning algorithm used to analyze a simple single-sensor experiment, in which the common source of variability is the only source of variability. (c) 2015 Elsevier Inc. All rights reserved.