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A whitening transformation or sphering transformation is a linear transformation that transforms a vector of random variables with a known covariance matrix into a set of new variables whose covariance is the identity matrix, meaning that they are uncorrelated and each have variance 1. [1]
The logarithm transformation and square root transformation are commonly used for positive data, and the multiplicative inverse transformation (reciprocal transformation) can be used for non-zero data. The power transformation is a family of transformations parameterized by a non-negative value λ that includes the logarithm, square root, and ...
Winsorizing or winsorization is the transformation of statistics by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers. It is named after the engineer-turned-biostatistician Charles P. Winsor (1895–1951). The effect is the same as clipping in signal processing.
Type I has also been called the skew-logistic distribution. Type IV subsumes the other types and is obtained when applying the logit transform to beta random variates. Following the same convention as for the log-normal distribution , type IV may be referred to as the logistic-beta distribution , with reference to the standard logistic function ...
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Most data files are adapted from UCI Machine Learning Repository data, some are collected from the literature. treated for missing values, numerical attributes only, different percentages of anomalies, labels 1000+ files ARFF: Anomaly detection: 2016 (possibly updated with new datasets and/or results) [331] Campos et al.
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Traditionally, data transformation has been a bulk or batch process, [6] whereby developers write code or implement transformation rules in a data integration tool, and then execute that code or those rules on large volumes of data. [7] This process can follow the linear set of steps as described in the data transformation process above.