It is important to apply a proper statistical analysis post-data acquisition such as data reduction, feature extraction and visualization.
For effective feature detection, it is good practice to first perform pre-processing steps, such as data reduction, noise filtering, background subtraction, mass calibration and retention time alignment, in order to clean up the data.
conducted observations of astronomical objects, as well as data reduction and analysis.
After that, machine learning techniques such as data cleaning, dimension reduction, and ensemble classifier were used to prioritize drug pairs bound to a common target.
Data reduction techniques, such as data compression, aggregation, and removal of redundancy should be implemented [69].
Lightweight theory-driven acquisition and preprocessing consists of activities such as data summarization, graphical representation, dimension reduction; and outlier detection.
These operations can be mapped into more complex operations, such as arithmetic, logical, or data reduction operations such as minimum or sum to be performed by the circulating register hardware (CRH) on the circulating copy of a register.
High-throughput genome-scale sequencing and microarray technologies generate huge amounts of data which challenge tasks such as dimension reduction, data compression, visual perception, data integration and extraction of biological information.
Various power reduction techniques, such as data-driven based on statistic results, nonuniform partition, precomputation, guarded evaluation, hierarchical FSM decomposition, TAG method, zero-block skipping, and clock gating, are adopted and integrated throughout the bitstream-residual decoder.
All the aforementioned scenarios can be present in applications such as pipeline inspection where data reduction is traditionally performed on-line using pipeline inspection gauges (PIG).
In biology these requirements are rarely fulfilled, requiring pre-processing of the data, such as noise reduction and detrending techniques, with the risk of convoluting the signal and losing valuable information.
