Tensor sufficient dimension reduction.
Tensor sufficient dimension reduction.
复制标题
张量充分降维。
DOI：
10.1002/wics.1350
复制
发表时间：
2015
期刊：
影响因子：
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通讯作者：
Suslick,Kenneth
中科院分区：
文献类型：
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作者：
Zhong,Wenxuan;Xing,Xin;Suslick,Kenneth
Tensor是一个多路数组。随着近几十年来科学技术的飞速发展，在当今的许多科学研究和商业活动中，大量的张量观测被常规地收集、处理和存储。比色传感器阵列（CSA）数据就是这样一个例子。由于需要解决CSA数据中出现的数据分析挑战，我们提出了一个张量降维模型，该模型假设响应与所有张量预测器的投影之间存在非线性依赖关系。张量降维模型以顺序迭代的方式估计。将该方法应用于收集的来自10种细菌的150种致病菌和来自1种对照细菌的14种细菌的CSA数据。实验结果表明，该方法可大大提高CSA技术的灵敏度和特异性。计算机学报，2015,(7):178 - 184。doi: 10.1002 / wics。本文分为：数据科学的统计学习和探索方法>图像数据挖掘数据分析的统计和图形方法>非参数方法数据科学的统计学习和探索方法>模式识别
Tensor is a multiway array. With the rapid development of science and technology in the past decades, large amount of tensor observations are routinely collected, processed, and stored in many scientific researches and commercial activities nowadays. The colorimetric sensor array (CSA) data is such an example. Driven by the need to address data analysis challenges that arise in CSA data, we propose a tensor dimension reduction model, a model assuming the nonlinear dependence between a response and a projection of all the tensor predictors. The tensor dimension reduction models are estimated in a sequential iterative fashion. The proposed method is applied to a CSA data collected for 150 pathogenic bacteria coming from 10 bacterial species and 14 bacteria from one control species. Empirical performance demonstrates that our proposed method can greatly improve the sensitivity and specificity of the CSA technique.WIREs Comput Stat2015, 7:178–184. doi: 10.1002/wics.1350This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Image Data MiningStatistical and Graphical Methods of Data Analysis > Nonparametric MethodsStatistical Learning and Exploratory Methods of the Data Sciences > Pattern Recognition