Sentence examples for data transformation approaches from inspiring English sources

High correlation among the neighboring pixels, both spectrally and spatially in a multispectral image makes it indispensable to use relevant data transformation approaches, before performing image fusion.

All three approaches gave similar results (not shown) which led us to use the first two data transformation approaches for subsequent multivariate analysis.

In this article, a domain adaptation (DA) technique using artificial neural networks based classifiers has been proposed using two-level cluster mapping technique by integrating the common data transformation and transfer learning approaches in a single framework.

Similar associations were also confirmed using SEM and different approaches to data transformation and the treatment of missing data.

The objective of the project was to design a new conceptual approach to configurable data transformation, de-identification, and submission of health and genomic data sets.

While, a near-surface model from DCI approach needed a data transformation into frequency-slowness (f-p) domain to allow for dispersion curves picking per shot gather.

Due to strong correlation and an abnormality in data distribution, no substantial improvement in diagnostic power was found with the best linear combination by LDA approach even with data transformation.

However, decision tree model with either 4-h alore or its combination with 2-h or 3-h did not suffer in diagnostic utility compared with its counterpart models identified with either LDA or DF approach, regardless of data transformation.

Other studies have shown that methods based on initial transformation perform well only when this transformation was appropriate for the data (Briggs et al., 2005) and that extended GLM methods are likely to outperform the standard GLM and approaches based on initial data transformation (Basu 2005; Basu et al., 2006; Manning et al., 2005).

In this paper three different approaches were explored: 1. Implement the data transformation logic within the data pipeline.

It applies g-log transform (generalised log transformation) for data transformation to stabilise variance [ 18, 19].