In this study, dimensionality reduction algorithms are applied to examine the correlation dynamics in the cryptocurrency market in which the time-dependent correlation matrix sequence is transformed into a distance matrix. The results based on multidimensional scaling ($MDS$) analysis show that information related to dynamic structural changes in the correlation coefficient matrix can be reconstructed well in low-dimensional Euclidean space. In particular, we found that the t-distributed stochastic neighbor embedding ($t-SNE$) algorithm can effectively exhibit the correlation dynamics in a two-dimensional (2D) space, thus providing a visualization method for analyzing dynamics in the market. Finally, we extract the clusters generated by the $t-SNE$ algorithm using the $k$ nearest neighbor ($kNN$) network, and study the difference in the yield distribution of the periods corresponding to different clusters. Based on a comparison with the CCI 30 index, we determined that the components in the $kNN$ network correspond well to different states in the market. The results show that dramatic changes in the correlation matrix suggest that significant changes may occur in the distribution of yields, such as a decrease in the average return.

在这项研究中，降维算法被应用于检查加密货币市场中的相关动态，在该市场中，时间相关的相关矩阵序列被转换为距离矩阵。基于多维缩放的结果（$\mathrm{中号}d\mathrm{小号}$）分析表明，在低维欧几里得空间中可以很好地重建与相关系数矩阵中动态结构变化有关的信息。特别是，我们发现t分布随机邻居嵌入（$Ť-\mathrm{小号}ñË$）算法可以有效地展示二维（2D）空间中的相关动力学，从而提供一种可视化的方法来分析市场中的动力学。最后，我们提取由$Ť-\mathrm{小号}ñË$ 使用的算法 $ķ$ 最近的邻居 （$ķññ$）网络，并研究与不同集群相对应的周期的收益分配差异。根据与CCI 30指数的比较，我们确定$ķññ$网络与市场中的不同州非常吻合。结果表明，相关矩阵的巨大变化表明，收益率分布可能发生重大变化，例如平均收益率下降。