The l1 norm, also known as the Manhattan norm or taxicab norm, measures the distance between two points in a space by summing the absolute differences of their coordinates. This concept is particularly important in signal processing and data analysis because it emphasizes sparsity and promotes solutions that have fewer non-zero elements, which is key for compressing data and accurately recovering signals.
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