论文标题
关于用于补丁缝合方法的调谐参数的选择
On the Selection of Tuning Parameters for Patch-Stitching Embedding Methods
论文作者
论文摘要
尽管像主成分分析一样，经典缩放是无参数的，但其他用于嵌入多元数据的方法需要选择一个或几个调谐参数。由于情况的无监督性，这种调整可能很困难。我们提出了一种简单，几乎明显的方法来监督调整参数的选择：最大程度地减少压力的概念。我们将这种方法应用于原型贴片插入方法中斑块大小的选择，包括在多维缩放（又称网络本地化）设置和降低性降低（aka歧管学习）设置中。在我们的研究中，我们发现了一种新的偏见 - 差异权衡现象。
While classical scaling, just like principal component analysis, is parameter-free, other methods for embedding multivariate data require the selection of one or several tuning parameters. This tuning can be difficult due to the unsupervised nature of the situation. We propose a simple, almost obvious, approach to supervise the choice of tuning parameter(s): minimize a notion of stress. We apply this approach to the selection of the patch size in a prototypical patch-stitching embedding method, both in the multidimensional scaling (aka network localization) setting and in the dimensionality reduction (aka manifold learning) setting. In our study, we uncover a new bias--variance tradeoff phenomenon.
