Space-in-Time and Time-in-Space Self-Organizing Maps for Exploring Spatiotemporal Patterns
نویسندگان
چکیده
منابع مشابه
Space-in-Time and Time-in-Space Self-Organizing Maps for Exploring Spatiotemporal Patterns
Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst’s expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial di...
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Interpreting Self - Organizing Maps through Space – Time Data Models
Self-organizing maps (SOMs) are a technique that has been used with high-dimensional data vectors to develop an archetypal set of states (nodes) that span, in some sense, the high-dimensional space. Noteworthy applications include weather states as described by weather variables over a region and speech patterns as characterized by frequencies in time. The SOM approach is essentially a neural n...
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Deriving patterns and relations from large multivariate and multi-temporal datasets to acquire knowledge about real world processes is not trivial. To understand the content of such datasets current analytical tools do offer interesting solutions, but an integrated approach is lacking. Here we introduce a visual integrated solution that allows one to explore and analyze the data at hand. The ap...
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We review a recent extension of the self-organizing map (SOM) for temporal structures with a simple recurrent dynamics leading to sparse representations, which allows an efficient training and a combination with arbitrary lattice structures. We discuss its practical applicability and its theoretical properties. Afterwards, we put the approach into a general framework of recurrent unsupervised m...
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ژورنال
عنوان ژورنال: Computer Graphics Forum
سال: 2010
ISSN: 0167-7055
DOI: 10.1111/j.1467-8659.2009.01664.x