Matrix Factorization and Latent Semantic Indexing Background Diagonal decomposition -example Recall The eigenvectors and form左 Inverting, we have VE Recall UU-1=1. Then, S=UAU7Matrix Factorization and Latent Semantic Indexing 12 Diagonal decomposition - example Recall ; 1, 3. 12 21 1 =2 =      S=   The eigenvectors and form         − 1 1         1 1       − = 1 1 1 1 U Inverting, we have       − − = 1/2 1/2 1 1/2 1/2 U Then, S=UU–1 =       −             − 1/21/2 1/21/2 03 10 11 11 Recall UU–1 =I. Background