Incremental eigen decomposition
Eigen decomposition is a central mathematical tool in many pattern recognition and machine learning techniques. However, it becomes computationally infeasible in the pres ence of a large set of high-dimensional samples. Moreover, in hig hly dynamic domains with a continual supply of new samples, an incremental approach that keeps on updating the eigen decom position will be more desirable than the traditional approach of simply re-computing the decomposition from scratch. In this paper, by using a method for updating singular value decompositions, we propose a procedure to obtain an approximate eigen decomposition by processing the samples in chunks or sequentially. On applying this to principal component anal ysis for image denoising, experimental results show that this restr icted form of updating can still achieve comparable performance as the batch version.