Non-linear downsampling is a technique used to reduce data size while retaining essential information by varying the sampling rate in different regions of the data. It emphasizes preserving key features and complex patterns which might be lost through linear downsampling, often using adaptive algorithms to dynamically determine where and how much to sample.