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People can understand complex structures if they relate to more isolated yet understandable concepts. Despite this fact, popular pattern recognition tools, such as decision tree or production rule learners, produce only flat models which do not build intermediate data representations. On the other hand, neural networks typically learn hierarchical but opaque models. We show how constraining neurons'...
Nonnegative Matrix Factorization (NMF) is one of the most promising techniques to reduce the dimensionality of the data. This presentation compares the method with other popular matrix decomposition approaches for various pattern analysis tasks. Among others, NMF has been also widely applied for clustering and latent feature extraction. Several types of the objective functions have been used for NMF...
Occluded face recognition is one the most interesting problems of applied computer vision. Among many face recognition approaches, the Nonnegative Matrix Factorization (NMF) turns out to be one of the popular techniques especially for part-based learning. It aims to factorize a nonnegative data matrix into two nonnegative matrices and obtains a well approximated product using an objective function...
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