About
Contact & Profiles
A5108010369
Research Areas
- Stochastic Gradient Optimization Techniques
- Tensor decomposition and applications
- Sparse and Compressive Sensing Techniques
Low-rank matrix approximations have recently gained broad popularity in scientific computing areas. They are used to extract correlations and remove noise from matrix-structured data with limited loss of information. Truncated singular value decomposition (SVD) is the main tool for low-rank approximation. However, applications such as latent semantic indexing where document collections dynamic over time, i.e. term subject repeated updates, SVD becomes prohibitive due high computational...
10.3906/mat-1707-14
article
EN
TURKISH JOURNAL OF MATHEMATICS
2018-07-24
Coming Soon ...