{excerpt}Singular Value Decomposition is a form of product decomposition of a matrix in which a rectangular matrix A is decomposed into a product U s V’ where U and V are orthonormal and s is a diagonal matrix.{excerpt} The values of A can be real or complex, but the real case dominates applications in machine learning. The most prominent properties of the SVD are:

In collaborative filtering and text retrieval, it is common to compute the partial decomposition of the user x item interaction matrix or the document x term matrix. This allows the projection of users and items (or documents and terms) into a common vector space representation that is often referred to as the latent semantic representation. This process is sometimes called Latent Semantic Analysis and has been very effective in the analysis of the Netflix dataset.

Dimension Reduction in Mahout:

See Also: