Dimension reduction is a term from the fields of Big Data and Smart Data, Artificial Intelligence, and Industry and Industry 4.0. It describes methods by which large, complex datasets are simplified by filtering out unimportant information. The goal is to filter out the important core messages from a lot of data and to make the analysis faster and clearer.
Imagine you have a huge Excel spreadsheet with thousands of columns – each column representing a property or a measurement, for example, in a modern factory. However, a lot of this data might be unimportant or contain similar information to other columns. With dimension reduction, you can shrink the data table so that only the most important columns remain. This makes the data easier to understand and saves storage space and processing time.
Dimension reduction is particularly important for identifying patterns and correlations in large datasets. This allows a company, for example, to more quickly find out which production steps really influence the quality of their products, without getting lost in too many details.
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