A Novel Fast Non-negative Matrix Factorization Algorithm and Its Application in Text Clustering

In non-negative matrix factorization, it is difficult to find the optimal non-negative factor matrix in each iterative update. However, with the help of transformation matrix, it is able to derive the optimal non-negative factor matrix for the transformed cost function. Transformation matrix based nonnegative matrix factorization method is proposed and analyzed. It shows that this new method, with comparable complexity as the priori schemes, is efficient in enhancing nonnegative matrix factorization and achieves better performance in NMF based text clustering.