slides
... • Projections along the N Eigen vectors with the largest Eigen values represent the N greatest “energy-carrying” components of the matrix • Conversely, N “bases” that result in the least square error are the N best Eigen vectors ...
... • Projections along the N Eigen vectors with the largest Eigen values represent the N greatest “energy-carrying” components of the matrix • Conversely, N “bases” that result in the least square error are the N best Eigen vectors ...
Matrix Factorization and Latent Semantic Indexing
... Latent Semantic Indexing (LSI) Term-document matrices are very large But the number of topics that people talk about is small (in some sense) ...
... Latent Semantic Indexing (LSI) Term-document matrices are very large But the number of topics that people talk about is small (in some sense) ...
Document
... understand the importance and extent of the ideas involved. The underlying idea can be used to describe the forces and accelerations in Newtonian mechanics and the potential functions of electromagnetism and the states of systems in quantum mechanics and the least-square fitting of experimental data ...
... understand the importance and extent of the ideas involved. The underlying idea can be used to describe the forces and accelerations in Newtonian mechanics and the potential functions of electromagnetism and the states of systems in quantum mechanics and the least-square fitting of experimental data ...
