Matrix Factorization and Latent Semantic Indexing Background Matrix vector multiplication Suggestion: the effect of small"eigenvalues is small If we ignored the smallest eigenvalue(1), then instead of (60 we would get 60 80 80 6 These vectors are similar (in cosine similarity etcMatrix Factorization and Latent Semantic Indexing 7 Matrix vector multiplication ▪ Suggestion: the effect of “small” eigenvalues is small. ▪ If we ignored the smallest eigenvalue (1), then instead of we would get ▪ These vectors are similar (in cosine similarity, etc.)  60 80 6            60 80 0           Background