绘制Kohonen地图-了解可视化 [英] Plotting the Kohonen map - Understanding the visualization

问题描述

针对SOM的Kohonen算法说明了如何针对每个训练示例调整最佳响应神经元及其邻居的输入权重.

the Kohonen algorithm for SOMs says how to adjust the input weights of the best responsive neuron and its neighbours for each training example.

关于绘图，我剩下了[em]个特征空间维度的(地图神经元数量).如何减少二维图显示在各处?

When it comes to plotting I am left with (number of map neurons)-many vectors of feature space Dimension. How is this reduced to get the 2D-plots shown everywhere?

亲切的问候！

推荐答案

SOM是一种非监督式群集算法.因此，它表示相似的样本，在特征图上更近(也就是说，相似的样本将触发距离更近的节点).

The SOM is a non-supervised clustering algorithm. As such it represent similar samples, closer on the feature map (this is, similar samples will fire nodes that are closer together).

因此，假设您有10000个样本，每个样本具有10个特征，以及2d-SOM为20x20x10(400个节点，具有10个特征).因此，在训练之后，您将10000个样本聚集成了400个节点.此外，您可以尝试通过例如U矩阵(表示节点权重向量与其最邻近邻居之间的平均距离的图)在SOM特征图上标识相似区域，或通过命中图"(表示该节点被选为最佳匹配单位的次数的地图-用于训练数据的BMU.

So lets assume you have 10000 samples with 10 features each, and a 2d-SOM of 20x20x10 (400 nodes with 10 features). After training you therefore clustered 10000 samples into 400 nodes. Further, you can try to identify similar regions on the SOM feature map through for example the U-Matrix (map representing the average distance between the node's weight vector and its closest neighbours), or eliminate non-useful nodes through the Hit-Map (map representing the number of times the node was chosen as the best matching unit - BMU for the training data).

因此，无需任何预处理，您可以减少25倍，甚至可以减少25倍.

So without any preprocessing you achieved a reduction of 25 times, and with some you may even achieve more.

有关详细说明的答案，请参见解释自组织图，如@lejlot

An for a more elaborated answer see Interpreting a Self Organizing Map as indicated by @lejlot

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