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 ...

• 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)
• Keywords: Computer Vision, CVPR 2009, Multiplicative Nonnegative Graph Embedding