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Rui Zhu;Kazuhiro Fukui;Jing-Hao Xue
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作者：
Rui Zhu;Kazuhiro Fukui;Jing-Hao Xue
类类比软独立建模（SIMCA）是一种广泛使用的光谱数据分类子空间方法。然而，由于SIMCA中的类子空间是独立建立的，因此忽略了类间的判别信息。一个有吸引力的补救措施是首先将原始数据投影到更具鉴别力的子空间。为此，探索生成矩阵中的类子空间之间的信息的广义差异子空间（GDS）可以是一个强有力的候选者。然而，由于类别子空间（无限尺度）和类别（有限尺度）之间的差异，GDS选择的特征向量对于分类类别的样本可能也没有区别。因此，在本文中，我们提出了一个判别有序子空间（DOS）：不同于GDS，我们的DOS选择的特征向量具有高的区分能力之间的类，而不是类的子空间。在三个真实的光谱数据集上的实验表明，
Soft independent modelling of class analogy (SIMCA) is a widely-used sub-space method for spectral data classification. However, since the class sub-spaces are built independently in SIMCA, the discriminative between-class information is neglected. An appealing remedy is to first project the original data to a more discriminative subspace. For this, generalised difference sub-space (GDS) that explores the information between class subspaces in the generating matrix can be a strong candidate. However, due to the difference between a class subspace (of infinite scale) and a class (of finite scale), the eigenvectors selected by GDS may not also be discriminative for classifying samples of classes. Therefore in this paper, we propose a discriminatively ordered subspace (DOS): different from GDS, our DOS selects the eigenvectors with high discriminative ability between classes rather than between class subspaces. The experiments on three real spectral datasets demonstrate