The structural information in high-dimensional transposable data allows us to write the data recorded for each subject in a matrix such that both the rows and the columns correspond to variables of interest. One important problem is to test the null hypothesis that the mean matrix has a particular structure without ignoring the dependence structure among and/or between the row and column variab...

The row by row decoupling problem (RRDP) for descriptor systems is considered using proportional state feedback and input transformation. Necessary and sufficient conditions for the solvability of the RRDP are provided. These solvability conditions can be readily verified. A constructive solution to the RRDP is given so that the desired feedback and input transformation matrices can be obtained...

Each ballot is first transformed into a square matrix with a row and column for each candidate. Each cell in the matrix represents a pairwise preference of the row candidate to the column candidate. A value of –1 indicates that the row candidate is preferred, whereas a 0 indicates the column candidate is preferred. By summing the column for a candidate and multiplying by –1, you can recover the...

Subspace analysis such as the Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are widely used feature extraction methods for face recognition. However, since most of them employ holistic basis, local information can not be represented in the subspace. Therefore, in general, they cannot cope with the occlusion problem in face recognition. In this paper, we propose a new...

In this paper, we research the run-time sparse matrix data transformation from Compressed Row Storage (CRS) to Coordinate (COO) storage and an ELL (ELLPACK/ITPACK) format with OpenMP parallelization for sparse matrix-vector multiplication (SpMV). We propose an auto-tuning (AT) method by using the Dmat i Rell graph, which plots the derivation/average for the number of non-zero elements per row (...