Toward ultimate pattern recognition by massive instances

这项研究的远大目标是了解性格模式的真实分布。理想情况下，如果我们可以收集所有可能的字符模式，我们就可以完全了解它们在图像空间中的分布情况。此外，我们还拥有完美的字符识别器，因为我们知道任何字符图像的正确类别。当然，收集所有这些模式实际上是不可能的，但是，如果我们大量收集特征模式并分析分布如何随着模式的增加而变化，我们将能够渐近地估计真实分布。为此，我们使用了822,714个手工打磨的手写数字模式。这些模式的分布是通过最近邻分析和网络分析来观察的，这两种分析都不做任何近似（比如低维表示），因此不会破坏分布的细节。

he ambitious goal of this research is to understand the real distribution of character patterns. Ideally, if we can collect all possible character patterns, we can totally understand how they are distributed in the image space. In addition, we also have the perfect character recognizer because we know the correct class for any character image. Of course, it is practically impossible to collect all those patterns - however, if we collect character patterns massively and analyze how the distribution changes according to the increase of patterns, we will be able to estimate the real distribution asymptotically. For this purpose, we use 822,714 manually ground-truthed handwritten digit patterns. The distribution of those patterns are observed by nearest neighbor analysis and network analysis, both of which do not make any approximation (such as low-dimensional representation) and thus do not corrupt the details of the distribution.