 Self-similarity matrix

A self-similarity matrix is a mathematical representation that captures the similarity between different segments of a single data set, such as time series data, images, or text. It is particularly useful in various fields including signal processing, computer vision, and natural language processing. ### Key Characteristics: 1. **Definition**: The self-similarity matrix is typically constructed by computing the similarity (or distance) between different segments or pieces of the same data.