Name
Abstract | ||
|---|---|---|
Confronted with the high-dimensional tensor-like visual data, we derive a method for the decomposition of an observed tensor into a low-dimensional structure plus unbounded but sparse irregular patterns. The optimal rank-(R1,R2, ...Rn) tensor decomposition model that we propose in this paper, could automatically explore the low-dimensional structure of the tensor data, seeking optimal dimension and basis for each mode and separating the irregular patterns. Consequently, our method accounts for the implicit multi-factor structure of tensor-like visual data in an explicit and concise manner. In addition, the optimal tensor decomposition is formulated as a convex optimization through relaxation technique. We then develop a block coordinate descent (BCD) based algorithm to efficiently solve the problem. In experiments, we show several applications of our method in computer vision and the results are promising. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15558-1_57 | ECCV (3) |
Keywords | Field | DocType |
error correction,optimum subspace,tensor data,optimal tensor decomposition,implicit multi-factor structure,method account,high-dimensional tensor-like visual data,optimal rank,tensor decomposition model,observed tensor,low-dimensional structure,optimal dimension,convex optimization | Subspace topology,Tensor,Tensor (intrinsic definition),Matrix completion,Computer science,Cartesian tensor,Artificial intelligence,Coordinate descent,Multilinear subspace learning,Convex optimization,Machine learning | Conference |
Volume | ISSN | ISBN |
6313 | 0302-9743 | 3-642-15557-X |
Citations | PageRank | References |
33 | 1.31 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yin Li | 1 | 797 | 35.85 |
| Junchi Yan | 2 | 891 | 83.36 |
| Yue Zhou | 3 | 176 | 11.68 |
| Jie Yang | 4 | 868 | 87.15 |