To construct a self-similarity matrix, one first transforms a data series into an ordered sequence of
feature vectors V = (v_1, v_2, \ldots, v_n) , where each vector v_i 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 : S(j,k) = s(v_j, v_k) \quad j,k \in (1,\ldots,n) where s(v_j, v_k) is a function measuring the similarity of the two vectors, for instance, the
inner product s(v_j, v_k) = v_j \cdot v_k. Then similar segments of feature vectors will show up as path of high similarity along diagonals of the matrix. Similarity plots are used for action recognition that is invariant to point of view ==Example==