Low-Rank Physical Model Recovery From Low-Rank Signal Approximation - 2017 PROJECT TITLE :Low-Rank Physical Model Recovery From Low-Rank Signal Approximation - 2017ABSTRACT:This work presents a mathematical approach for recovering a physical model from a low-rank approximation of measured information obtained via the singular price decomposition (SVD). The general kind of a low-rank physical model of the data is typically known, so the presented approach learns the correct rotation and scaling matrices from the singular vectors and singular values of the SVD in order to recover the low-rank physical model of the information from the SVD approximation. By recovering the low-rank physical model, it becomes attainable to use specific knowledge of the model to extract meaningful data for the physical application being studied. This work is useful for processing wide-band electromagnetic induction data-the motivating application. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Large-Scale Kernel-Based Feature Extraction via Low-Rank Subspace Tracking on a Budget - 2018 Scalable Solvers of Random Quadratic Equations via Stochastic Truncated Amplitude Flow - 2017
