matrixfactorization is widely used in recommendation systems, text mining, face recognition and computer vision. As one of the most popular methods, nonnegativematrixfactorization and its incremental variants have ...
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matrixfactorization is widely used in recommendation systems, text mining, face recognition and computer vision. As one of the most popular methods, nonnegativematrixfactorization and its incremental variants have attracted much attention. The existing incremental algorithms are established based on the assumption of samples are independent and only update the new latent variable of weighting coefficient matrix when the new sample comes, which may lead to inferior solutions. To address this issue, we investigate a novel incremental nonnegativematrixfactorization algorithm based on correlation and graph regularizer (ICGNMF). The correlation is mainly used for finding out those correlated rows to be updated, that is, we assume that samples are dependent on each other. We derive the updating rules for ICGNMF by considering the correlation. We also present tests on widely used image datasets, and show ICGNMF reduces the error by comparing other methods.
MANY dimensionality reduction algorithms have been proposed easing both tasks of visualization and classification in high dimension problems. Despite the different motivations they can be cast in a graph embedding fra...
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ISBN:
(纸本)9781424496365
MANY dimensionality reduction algorithms have been proposed easing both tasks of visualization and classification in high dimension problems. Despite the different motivations they can be cast in a graph embedding framework. In this paper we address weighted graph subspace learning methods for bankruptcy analysis. The rationale behind re-embedding the data in a lower dimensional space that would be better filled is twofold: to get the most compact representation (visualization) and to make subsequent processing of data more easy (classification). The approaches used, graphregularized Non-Negative matrixfactorization (GNMF) and Spatially Smooth Subspace Learning (SSSL), construct an affinity weight graphmatrix to encode geometrical information and to learn in the training set the subspace models that enhance visualization and are able to ease the task of bankruptcy prediction. The experimental results on a real problem of French companies show that from the perspective of financial problem analysis the methodology is quite effective.
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