Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPRW.2009.5206574
Title: Multiplicative nonnegative graph embedding
Authors: Wang, C.
Song, Z. 
Yan, S. 
Zhang, L.
Zhang, H.-J.
Issue Date: 2009
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 [14] for reaping the benefits from both nonnegative data factorization and the specific purpose characterized by the intrinsic and penalty graphs [13]. 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 [14] 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
URI: http://scholarbank.nus.edu.sg/handle/10635/71060
ISBN: 9781424439935
DOI: 10.1109/CVPRW.2009.5206574
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