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On the Preservation of Spatio-temporal Information in Machine Learning Applications
[article]
2020
arXiv
pre-print
In conventional machine learning applications, each data attribute is assumed to be orthogonal to others. Namely, every pair of dimension is orthogonal to each other and thus there is no distinction of in-between relations of dimensions. However, this is certainly not the case in real world signals which naturally originate from a spatio-temporal configuration. As a result, the conventional vectorization process disrupts all of the spatio-temporal information about the order/place of data
arXiv:2006.08321v1
fatcat:fokwd5vtvrdipkyc2tmavaivju