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Multi-Sensor Fusion via Reduction of Dimensionality
[article]
2012
arXiv
pre-print
Large high-dimensional datasets are becoming more and more popular in an increasing number of research areas. Processing the high dimensional data incurs a high computational cost and is inherently inefficient since many of the values that describe a data object are redundant due to noise and inner correlations. Consequently, the dimensionality, i.e. the number of values that are used to describe a data object, needs to be reduced prior to any other processing of the data. The dimensionality
arXiv:1211.2863v1
fatcat:rtqwctr4ynghpd2jblcvfvgpuq