Classical matrix factorization techniques such as the LU, QR, EVD and SVD or their variants have been used with great success in various data analytic applications. These include information retrieval, text mining, bioinformatics, computer graphics, computer vision & product recommendations. In the past few years, internet applications have thrown some new challenges to the numerical linear algebraists - unprecedented features and problems inherent in internet data have rendered traditional matrix factorization techniques less effective. Some of the new issues that arise in internet data mining include, prohibitively large data size - internet data sets are often much too large to allow multiple random accesse, massively incomplete data - a significant proportion of the data may be missing, novel structures in data - most importantly, datasets whose underlying structure cannot be adequately unraveled by LU, QR, EVD, or SVD have become increasingly common, not just in internet applications but also in other scientific & engineering fields. In the last few years, several new matrix factorizations have been proposed to deal with these issues. Some notables ones include, Nonnegative Matrix Factorization, Maximum Margin Matrix Factorization, Matrix Subspace Factorization & Sparse Overcomplete Factorization (Compressed Sensing). The key difference between these & the classical matrix factorizations is that they are not rank-revealing in the traditional sense but instead they reveal other properties of the structure under consideration.