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Both applications support manipulating and changing the scale of the projected data items.
Line-of-sight polar interpolation (LOSPI) is applied to the projected data.
The projected data, with enhanced sparsity, is then processed using l1 norm reconstruction techniques.
Meanwhile, we design a linear classifier using the projected data to guide the label for efficient image classification task.
From this subspace, a standard SVM can be applied for classification using the projected data from both domains.
The projected components are statistically uncorrelated and the rank of the projected data matrix is equal to the data dimensionality.
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If the chosen σ is too large, the projecting data is likely to collapse to a single point.
The resulting projected data were reconstructed and images with 3400 × 3400 pixels/slice were created.
First, we conduct trend and stationarity tests for the observed and projected data.
Most of the projects data were also obtained from TxDOT.
Monitoring of the project data takes place continuously at the DCC.
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