Dimension reduction is a term used in the fields of big data and smart data, artificial intelligence, industry and Factory 4.0. It describes methods used to simplify large, complex data sets by filtering out unimportant information. The aim is to filter out the important core statements from a large amount of data and make the analysis faster and clearer.
Imagine you have a huge Excel spreadsheet with thousands of columns - each column representing a property or measurement, such as in a modern factory. However, much of this data may be irrelevant or contain similar information to other columns. Dimension Reduction can be used to reduce the size of the data table so that only the most important columns remain. This makes the data easier to understand and saves storage space and computing time.
Dimension reduction is particularly important for recognising patterns and correlations in large amounts of data. For example, a company can find out more quickly which production steps really influence the quality of its products without getting lost in too many details.
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