Evaluation of the Neural-network-based Method to Discover Sets and Representatives of Nonlinearly Dependent Variables
Evaluation of the Neural-network-based Method to Discover Sets and Representatives of Nonlinearly Dependent Variables
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基于神经网络的非线性因变量发现集和代表方法的评估
DOI：
10.1109/cybconf51991.2021.9464151
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Then Patrick
中科院分区：
文献类型：
--
作者：
Ohsaki Miho;Sasaki Hayato;Kishimoto Naoya;Katagiri Shigeru;Ohnishi Kei;Sebastian Yakub;Then Patrick
在各种领域中，需要识别哪些变量是相关的，并且已经研究了变量相关性度量。大多数这样的措施检测配对变量之间的线性或一定范围的非线性依赖。为了超越它们，我们在过去的研究中提出了一种基于神经网络回归，组套索和信息聚合的方法。它可以检测多个变量之间的广泛的非线性依赖关系，并发现检测到的依赖关系的集合和代表。它的基本有效性已经使用合成的人工数据集包含一个单一的依赖。为了进一步评估本研究，我们进行了一个实验，使用那些包含多个依赖。所提出的方法成功地发现了集合和代表，其性能是强大的数据大小和噪声率。实验结果表明，该方法可以很好地处理困难的任务，以处理多依赖。
It is desired in a variety of fields to identify which variables are dependent, and variable dependence measures have been studied. The majority of such measures detect a linear or a certain range of nonlinear dependence between paired variables. To go beyond them, a method based on Neural Network Regression, Group Lasso, and Information Aggregation has been proposed in our past study. It can detect a wide range of nonlinear dependences among multi variables and discover the sets and representatives of the detected dependences. Its fundamental effectiveness has already been examined using synthesized artificial datasets containing a single dependence. For further evaluation in the present study, we conducted an experiment using those containing multi dependences. The proposed method succeeded in discovering the sets and representatives, and its performance was robust to data size and noise rate. The experimental results suggested that the proposed method works well for difficult tasks to handle multi dependences.