Definition
To construct a self-similarity matrix, one first transforms a data series into a an ordered sequences of feature vectors, where each vector describes the relevant features of a data series in a given local interval. Then the self-similarity matrix is formed by computing the similarity of pairs of feature vectors
where is a function measuring the similarity of the two vectors, for instance, the inner product . Then similar segments of feature vectors will show up as path of high similarity along diagonals of the matrix.
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