摘要：In recent years, nonnegative matrix factorization (NMF) methods of a reduced image data representation
attracted the attention of computer vision community. These methods are considered as a convenient part-based
representation of image data for recognition tasks with occluded objects. A novel modification in NMF
recognition tasks is proposed which utilizes the matrix sparseness control introduced by Hoyer. We have
analyzed the influence of sparseness on recognition rates (RRs) for various dimensions of subspaces generated
for two image databases, ORL face database, and USPS handwritten digit database. We have studied the
behavior of four types of distances between a projected unknown image object and feature vectors in NMF subspaces
generated for training data. One of these metrics also is a novelty we proposed. In the recognition
phase, partial occlusions in the test images have been modeled by putting two randomly large, randomly
positioned black rectangles into each test image.
