Canonical variate analysis of high-dimensional spectral data
Canonical variate analysis of high-dimensional spectral data
复制标题
高维光谱数据的典型变量分析
DOI：
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发表时间：
1992
期刊：
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通讯作者：
H. Kiiveri
中科院分区：
文献类型：
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作者：
H. Kiiveri
这篇文章关注的是当变量多于观测值时，如何量化和表示组间差异。特别是，典型的变量分析时，数据包括在许多网格点采样的曲线被认为是。提出了一种新的方法，涉及取代通常奇异的组内变化矩阵的拟合矩阵是正定的。为了获得拟合矩阵，一类模型，沿着与相关的估计和模型选择程序，提出。结果被应用到实验数据，旨在评估有用的数据从便携式现场光谱仪区分可用的农田和受盐度影响的农田。
This article is concerned with quantifying and representing group differences when there are more variables than observations. In particular, canonical variate analysis when the data consist of curves sampled at many grid points is considered. A new method is proposed that involves replacing the usually singular within-groups variation matrix by a fitted matrix that is positive-definite. To obtain the fitted matrix, a class of models, along with associated estimation and model-selection procedures, is presented. The results are applied to experimental data designed to assess the usefulness of data from a portable field spectrometer for discriminating between usable farmland and farmland affected by salinity.