For many computer vision problems, the most time consuming component consists of nearest neighbor matching in high-dimensional spaces. There are no known exact algorithms for solving these high-dimensional problems that are faster than linear search
We derive an analytical expression of the best approximate solution in the leastsquares solution set SE of the matrix equation AXB + CY D = E to a given matrix pair (Xf, Yf ), where A, B, C, D, and E are given matrices of suitable sizes. Our work is
here is an ongoing research effort devoted to characterize the signal regularity metrics approximate entropy (ApEn) and sample entropy (SampEn) in order to better interpret their results in the context of biomedical signal analysis. Along with this
