Multivariate data visualization is an important way of understand multivariate data.
多元数据可视化是理解多元数据的一种重要手段。
A new multivariate data visualization method named point-score parallel coordinates (PSPC) is proposed in this paper.
提出了一种多元数据的点得分平行坐标表示及可视化分析方法。
You can carry out very comprehensive analysis of univariate and multivariate data based on linear, general linear, and mixed linear models.
你可以基于线性、广义线性、以及混合线性模型进行单变量和多变量数据的全面分析。
A subspace graphical representation model of multivariate data is proposed, which unites several traditional multivariate data visualization methods into the same representation framework.
本文提出多元数据的子空间坐标图表示模型，该模型可以将这些传统多元图表示方法统一到同一个表示框架。
However, these targets can not be performed easily especially with the complex multivariate data sets which can be obtained from most of the advanced chemical instruments and chemical plants.
然而，这些目标并不容易实现，特别是当我们处理从先进的化学仪器或化工厂获得的复杂多变量数据集时。
Statistical Hypotheses Testing Method for Fuzzy Multivariate Data;
本文给出了一个模糊多元数据的假设检验方法。
Pattern Recognition; Visualization; Multivariate Data; Graphical Representation; Geometric Algebra; Subspace Coordinates; Optimization.
模式识别；可视化；多元数据；图表示；几何代数；子空间坐标；优化。
Pattern Recognition; Visualization; Multivariate Data; Graphical Representation; Geometric Algebra; Subspace Coordinates; Optimization.
模式识别；可视化；多元数据；图表示；几何代数；子空间坐标；优化。
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