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首页> 外文期刊>Transactions in GIS: TG >The Nature and Classification of Unlabelled Neurons in the Use of Kohonen's Self-Organizing Map for Supervised Classification
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The Nature and Classification of Unlabelled Neurons in the Use of Kohonen's Self-Organizing Map for Supervised Classification

机译：使用Kohonen自组织图进行监督分类的未标记神经元的性质和分类

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摘要

Kohonen's Self-Organizing Map is a neural network procedure in which a layer of neurons is initialized with random weights, and subsequently organized by inspection of the data to be analyzed. The organization procedure uses progressive adjustment of weights based on data characteristics and lateral interaction such that neurons with similar weights will tend to spatially cluster in the neuron layer. When the SOM is associated with a supervised classification, a majority voting technique is usually used to associate these neurons with training data classes. This technique, however, cannot guarantee that every neuron in the output layer will be labelled, and thus causes unclassified pixels in the final map. This problem is similar to but fundamentally different from the problem of dead units that arises in unsupervised SOM classification (neurons which are never organized by the input data). In this paper we specifically address the problem and nature of unlabelled neurons in the use of SOM for supervised classification. Through a case study it is shown that unlabelled neurons are associated with unknown image classes and, most particularly, mixed pixels. It is also shown that an auxiliary algorithm proposed here for assigning classes to unlabelled neurons performs with the same success as that experienced with Maximum Likelihood.
机译：Kohonen的“自组织图”是一种神经网络程序，其中用随机权重初始化神经元层，然后通过检查要分析的数据进行组织。组织过程使用基于数据特征和横向交互作用的权重进行逐步调整，以使权重相似的神经元倾向于在神经元层中空间聚集。当SOM与监督分类相关联时，通常使用多数表决技术将这些神经元与训练数据类别相关联。但是，该技术不能保证输出层中的每个神经元都将被标记，从而在最终图中导致未分类的像素。此问题与无监督SOM分类（神经元永远不会由输入数据组织）中出现的失效单元问题相似，但从根本上不同。在本文中，我们专门解决在使用SOM进行监督分类时未标记神经元的问题和性质。通过案例研究表明，未标记的神经元与未知图像类别（尤其是混合像素）相关。还表明，此处提出的用于将类别分配给未标记的神经元的辅助算法的执行效果与最大似然法相同。

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