A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification
Links to Fileshttps://arxiv.org/abs/1808.08315
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Type of Work8 pages
journal articles preprints
Citation of Original PublicationWenbin Zhang, Jianwu Wang, Daeho Jin, Lazaros Oreopoulos, Zhibo Zhang, A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification,Version 2,2018 ,,https://arxiv.org/abs/1808.08315
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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. Not withstanding 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.
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