数据科学问题中的分形多样性方法
Method of fractal diversity in data science problems
摘要：
参数（SNR）得到区分高斯函数，在没有互相关的情况下，随机变量的分布，从其他功能，这使得有可能描述集体状态与强互相关的数据。确定了一维空间中的信噪比，并给出了一种基于闭环中Cantor尘分形变化的计算算法。该算法对初始数据集的线性变换是不变的，具有重整化群不变性，并确定了数据的互相关（集体效应）的强度。对集体状态的描述是普遍的，不依赖于数据相关性的性质，也不依赖于在没有数据相关性的情况下随机变量分布的普遍性。该方法适用于信息技术中获得的大量非高斯或奇异数据。在确认Koshland的假设，该方法的应用程序的强度数据的数字X-射线衍射光谱与计算的集体效应，使得有可能确定一个构象表现出生物活性。
英文摘要：
The parameter (SNR) is obtained for distinguishing the Gaussian function, the distribution of random variables in the absence of cross correlation, from other functions, which makes it possible to describe collective states with strong cross-correlation of data. The signal-to-noise ratio (SNR) in one-dimensional space is determined and a calculation algorithm based on the fractal variety of the Cantor dust in a closed loop is given. The algorithm is invariant for linear transformations of the initial data set, has renormalization-group invariance, and determines the intensity of cross-correlation (collective effect) of the data. The description of the collective state is universal and does not depend on the nature of the correlation of data, nor is the universality of the distribution of random variables in the absence of data correlation. The method is applicable for large sets of non-Gaussian or strange data obtained in information technology. In confirming the hypothesis of Koshland, the application of the method to the intensity data of digital X-ray diffraction spectra with the calculation of the collective effect makes it possible to identify a conformer exhibiting biological activity.
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