کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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411617 | 679578 | 2016 | 12 صفحه PDF | دانلود رایگان |

With the demand of high dimensional data analysis, data representation (or feature learning) has attracted more and more attention and becomes a central problem in pattern recognition and data mining. Non-negative Matrix Factorization (NMF) which is a useful data representation method makes great contribution in finding the latent structure of the data and leading to a parts-based representation by decomposing the data matrix into a few bases and encodings with the non-negative constraint. Considering the learned encodings from a statistical view by modeling the data points via ridge regression and minimizing the variance of the parameter, A-Optimal Non-negative Projection (ANP) improves the performance of NMF. However, it neglects the intrinsic geometric structure of the data. We introduce Hessian regularization and propose a novel method called A-Optimal Non-negative Projection with Hessian regularization (AHNP) to address this problem. Therefore, AHNP not only leads to parts-based and precise representations but preserves the intrinsic geometrical structure of the obtained subspace. We demonstrate the effectiveness of this novel algorithm through a set of evaluations on real world applications.
Journal: Neurocomputing - Volume 174, Part B, 22 January 2016, Pages 838–849