数据形状为(x，y，z)时如何进行聚类? [英] how to do clustering when the shape of data is (x,y,z)?

问题描述

假设我有10个单独的观测值，每个观测值的大小分别为(125,59).我想基于它们的2d特征矩阵((125,59))将这10个观测值分组.是否可以在不将每个观测值展平为125 * 59 1D矩阵的情况下进行?我什至不能实现PCA或LDA来进行特征提取，因为数据是高度可变的.请注意，我正在尝试通过自组织图或神经网络来实现聚类.深度学习和神经网络与所提出的问题完全相关.

suppose i have 10 individual observations each of size (125,59). i want to group these 10 observations based on their 2d feature matrices ((125,59)).Is this possible without flattening every observation to 125*59 1D matrix ? I cant even implement PCA or LDA for feature extraction because the data is highly variant. Please note that i am trying to implement clustering through self organizing maps or neural networks. Deep learning and neural networks are completely related to the question asked.

推荐答案

当然可以.

定义适当的距离度量.

然后计算10x10距离矩阵，并运行分层聚类.

Then compute the 10x10 distance matrix, and run hierarchical clustering.

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