A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification
A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification
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DOI：
10.1109/bigdata.2018.8622558
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发表时间：
2018-08
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影响因子：
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通讯作者：
Wenbin Zhang;Jianwu Wang;Daeho Jin;L. Oreopoulos;Zhibo Zhang
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文献类型：
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作者：
Wenbin Zhang;Jianwu Wang;Daeho Jin;L. Oreopoulos;Zhibo Zhang
自组织映射（SOM）是一种竞争型人工神经网络，它将训练样本的高维输入空间投影到一个保留拓扑关系的低维空间中。这使得SOM能够用于组织和可视化复杂数据集，并在众多不同应用的学科中得到了广泛应用。尽管其应用广泛，但自组织映射因其固有的随机性而令人困惑，即使每次使用相同的参数对相同的训练样本进行训练，它也会产生不同的SOM模式，从而引起其他领域从业者对可用性的担忧，并阻碍更多潜在用户在更广泛的领域探索基于SOM的应用。基于这一实际问题，我们提出一种确定性方法作为标准自组织映射的补充。根据理论设计，利用卫星云图数据进行的实验结果证明了所提方法具有有效且高效的组织和简化能力。
A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high-dimensional input space of the training samples into a low-dimensional space with the topology relations preserved. This makes SOMs supportive of organizing and visualizing complex data sets and have been pervasively used among numerous disciplines with different applications. Notwithstanding its wide applications, the self-organizing map is perplexed by its inherent randomness, which produces dissimilar SOM patterns even when being trained on identical training samples with the same parameters every time, and thus causes usability concerns for other domain practitioners and precludes more potential users from exploring SOM based applications in a broader spectrum. Motivated by this practical concern, we propose a deterministic approach as a supplement to the standard self-organizing map. In accordance with the theoretical design, the experimental results with satellite cloud data demonstrate the effective and efficient organization as well as simplification capabilities of the proposed approach.