Cluster Indicator Decomposition for Efficient Matrix Factorization

We propose a new clustering based low-rank matrix approximation method, Cluster Indicator Decomposition (CID), which yields more accurate low-rank approximations than previous commonly used singular value decomposition and other Nystrom style decompositions. Our model utilizes the intrinsic structures of data and theoretically be more compact and accurate than the traditional low rank approximation approaches. The reconstruction in CID is extremely fast leading to a desirable advantage of our method in large-scale kernel machines (like Support Vector Machines) in which the reconstruction of the kernels needs to be frequently computed. Experimental results indicate that our approach compress images much more efficiently than other factorization based methods. We show that combining our method with Support Vector Machines obtains more accurate approximation and more accurate prediction while consuming much less computation resources.