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A novel method is introduced for exploiting the support vector machine constraints in nonnegative matrix factorization. The notion of the proposed method is to find the projection matrix that projects the data to a low-dimensional space so that the data projections between the two classes are separated with maximum margin. Experiments were performed for the task of eating and drinking activity classification...
A novel method is introduced for exploiting the support vector machine and additional discriminant constraints in nonnegative matrix factorization. The notion of the proposed method is to find the projection matrix that projects the data to a low-dimensional space so that the data projections have minimum within-class variance, maximum between-class variance and the data projections between the two...
Non-negative Matrix Factorization (NMF) is among the most popular subspace methods widely used in a variety of image processing problems. Recently, a discriminant NMF method that incorporates Linear Discriminant Analysis criteria and achieves an efficient decomposition of the provided data to its discriminant parts has been proposed. However, this approach poses several limitations since it assumes...
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