Non-negative matrix factorization using constrained optimization with applications

The novel algorithm proposed in this thesis will improve the non-negative matrix factorization. It will help factorizing and categorizing large data matrices. This algorithm will have application in facial recognition, document and text clustering. Processing high dimensional data will be easier using this algorithm.