Name
Abstract | ||
|---|---|---|
This work developed novel complex matrix factorization methods for face recognition; the methods were complex matrix factorization (CMF), sparse complex matrix factorization (SpaCMF), and graph complex matrix factorization (GraCMF). After real-valued data are transformed into a complex field, the complex-valued matrix will be decomposed into two matrices of bases and coefficients, which are derived from solutions to an optimization problem in a complex domain. The generated objective function is the real-valued function of the reconstruction error, which produces a parametric description. Factorizing the matrix of complex entries directly transformed the constrained optimization problem into an unconstrained optimization problem. Additionally, a complex vector space with N dimensions can be regarded as a 2N-dimensional real vector space. Accordingly, all real analytic properties can be exploited in the complex field. The ability to exploit these important characteristics motivated the development herein of a simpler framework that can provide better recognition results. The effectiveness of this framework will be clearly elucidated in later sections in this paper. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computer Vision and Pattern Recognition | Algebra,Matrix (mathematics),Computer science,Matrix decomposition,Hollow matrix,Factorization,Incomplete LU factorization,Eigendecomposition of a matrix,State-transition matrix,Block matrix |
DocType | Volume | Citations |
Journal | abs/1612.02513 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Viet-Hang Duong | 1 | 2 | 2.75 |
| Yuan-Shan Lee | 2 | 23 | 8.51 |
| Bach-Tung Pham | 3 | 1 | 1.37 |
| Seksan Mathulaprangsan | 4 | 2 | 1.71 |
| Pham The Bao | 5 | 22 | 7.70 |
| Jia-Ching Wang | 6 | 23 | 7.47 |