Name
Abstract | ||
|---|---|---|
In this paper, we study the problem of nonnegative graph embedding, originally investigated in [J. Yang et al., 2008] 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 [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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CVPR.2009.5206865 | CVPR |
Keywords | Field | DocType |
image processing,multiplicative nonnegative graph embedding,computer graphics,penalty graphs,multiplicative iterative procedure,tensor representation,realistic image occlusion,matrix inversion,matrix decomposition,matrix inverse calculation,graph theory,m-matrix,nonnegative data factorization,tensors,embedded computing,encoding,robustness,tensile stress,graph embedding,m matrix,factor graph,psychology | Graph theory,Discrete mathematics,Combinatorics,Embedding,Multiplicative function,Nonnegative matrix,Tensor,Computer science,Graph embedding,Matrix decomposition,Factorization | Conference |
Volume | Issue | ISSN |
2009 | 1 | 1063-6919 |
ISBN | Citations | PageRank |
978-1-4244-3992-8 | 6 | 0.68 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changhu Wang | 1 | 1296 | 70.36 |
| Zheng Song | 2 | 250 | 11.25 |
| Shuicheng Yan | 3 | 9701 | 359.54 |
| Lei Zhang | 4 | 2533 | 164.29 |
| Hong-Jiang ZHANG | 5 | 17378 | 1393.22 |