Multiplicative Nonnegative Graph Embedding

14 years 9 months ago
Multiplicative Nonnegative Graph Embedding
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 ...
Changhu Wang (University of Science and Technology
Added 09 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Changhu Wang (University of Science and Technology of China), Zheng Song (National University of Singapore), Shuicheng Yan (National University of Singapore), Lei Zhang (Microsoft Research Asia), Hong-Jiang Zhang (Microsoft Corporation)
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