Classification using spatial shrunken centroids.
使用空间收缩质心进行分类。
本节演示了我们在 Cardinal 中引入的空间收缩质心方法进行统计分析的空间收缩质心方法。在这种方法中，我们通过空间平滑调整最近的收缩质心分类器 [2]。该方法使用统计正则化将每个条件的平均谱向全局平均谱缩小。这种收缩允许重要质量的自动特征选择。然后它通过将像素的质谱与每个条件下缩小的平均光谱进行比较来对像素进行分类。空间平滑使用从空间感知聚类 [3] 改编的权重，包括高斯权重，以及试图解释局部结构的自适应权重。
This section demonstrates the spatial shrunken centroids classification method for statistical analysis we introduce in Cardinal in the spatial Shrunken Centroids method. In this method, we adapt the nearest shrunken centroids classifier [2] with spatial smoothing. This method uses statistical regularization to shrink each condition's mean spectrum toward the global mean spectrum. This shrinkage allows automated feature selection of important masses. It then classifies pixels by comparing their mass spectra to the shrunken mean spectra of each conditions. The spatial smoothing uses weights adapted from spatially-aware clustering [3], including Gaussian weights, and adaptive weights that attempt to account for local structure.
The parameters to be explicitly provided in the spatialShrunkenCentroids method are:在 spatialShrunkenCentroids 方法中要明确提供的参数是：
� r: The neighborhood smoothing radius ˆr：邻域平滑半径
� s: The shrinkage parameter ˆs: 收缩参数
The s parameter is the shrinkage parameter that enforces sparsity. As s increases, fewer mass features (m=z values) will be used by the classifier, and only the informative mass features will be retained.s 参数是强制稀疏的收缩参数。随着 s 的增加，分类器将使用更少的质量特征（m=z 值），并且只会保留信息丰富的质量特征。
For a detailed explanation of the shrinkage parameter s, see [2] and [4].有关收缩参数 s 的详细说明，请参见 [2] 和 [4]。
Clustering can also be performed if no response variable y is given, by providing an additional parameter k for the initial number of clusters. See the clustering work below for details.如果没有给出响应变量 y，也可以通过为初始聚类数提供附加参数 k 来执行聚类。有关详细信息，请参阅下面的聚类工作。
2.5.1 Cross-validation 2.5.1 交叉验证
