10 Facts About Data reduction

Data reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form.

The purpose of data reduction can be two-fold: reduce the number of data records by eliminating invalid data or produce summary data and statistics at different aggregation levels for various applications.

The data reduction is often undertaken in the presence of reading or measurement errors.

The on-board data reduction encompasses co-adding the raw frames for thirty minutes, reducing the bandwidth by a factor of 300.

For example, in the context of epilepsy diagnosis, data reduction has been used to increase the battery lifetime of a wearable EEG device by selecting and only transmitting, EEG data that is relevant for diagnosis and discarding background activity.

Dimensionality reduction helps reduce noise in the data and allows for easier visualization, such as the example below where 3-dimensional data is transformed into 2 dimensions to show hidden parts.

One method of dimensionality reduction is wavelet transform, in which data is transformed to preserver relative distance between objects at different levels of resolution, and is often used for image compression.

Numerosity Data reduction can be split into 2 groups: parametric and non-parametric methods.

Data reduction can be obtained by assuming a statistical model for the data.

Classical principles of data reduction include sufficiency, likelihood, conditionality and equivariance.