Data Preprocessing Using Non Negative Matrix Factorization for Multimedia Mining

Abstrect Matrix factorization techniques have been frequently applied in information retrieval, computer vision and pattern recognition. Among them, Non-negative Matrix Factorization (NMF) has received considerable attention due to its psychological and physiological interpretation of naturally occurring data whose representation may be parts-based in the human brain. On the other hand, from the geometric perspective, the data is usually sampled from a low dimensional manifold embedded in a high dimensional ambient space. One hopes then to find a compact representation which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. This Paper presents novel algorithm, called Graph Regularized Non-negative Matrix Factorization of image for multimedia Mining (GNMFMM), In GNMFMM, an affinity graph is constructed to encode the image for multimedia, and seek a matrix factorization which respects the graph structure.