The SVD of the matrix W is then defined as the product of three matrices, where the columns of B contains the eigenvectors of W and / is a diagonal matrix containing the eigenvalues in descending order: The eigenvectors are normalized to have length 1 and orthogonal, which means that they satisfy the following condition: B TB= I. Decomposing a regular matrix into a product of three other matrices is not too interesting.
the truncated SVD matrix is used to show the high coherence relationship of the segments in the document, and to estimate the structure in segments across the document.