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|Title:||Multiplicative nonnegative graph embedding|
|Source:||Wang, C.,Song, Z.,Yan, S.,Zhang, L.,Zhang, H.-J. (2009). Multiplicative nonnegative graph embedding. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009 : 389-396. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPRW.2009.5206574|
|Abstract:||In this paper, we study the problem of nonnegative graph embedding, originally investigated in  for reaping the benefits from both nonnegative data factorization and the specific purpose characterized by the intrinsic and penalty graphs . Our contributions are two-fold. On the one hand, we present a multiplicative iterative procedure for nonnegative graph embedding, which significantly reduces the computational cost compared with the iterative procedure in  involving the matrix inverse calculation of an M-matrix. On the other hand, the nonnegative graph embedding framework is expressed in a more general way by encoding each datum as a tensor of arbitrary order, which brings a group of byproducts, e.g., nonnegative discriminative tensor factorization algorithm, with admissible time and memory cost. Extensive experiments compared with the state-of-the-art algorithms on nonnegative data factorization, graph embedding, and tensor representation demonstrate the algorithmic properties in computation speed, sparsity, discriminating power, and robustness to realistic image occlusions. ©2009 IEEE.|
|Source Title:||2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009|
|Appears in Collections:||Staff Publications|
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