The theory of matrices: with applications Miron Tismenetsky, Peter Lancaster
Publisher: AP

Best cited paper on the Web of Science. In various applications in signal processing and machine learning, the model of interest is known to be structured in mph{several} ways at the same time, for example, a matrix that is simultaneously sparse and low-rank. Recent applications in which the problem arises include reconstructing 20th century sea levels, genetic evaluations for thoroughbred horse breeding, modelling public health data sets, modelling storm damage of buildings, and a Kriging model for reservoirs. The CRIT framework for identifying cross patterns in systems biology and application to chemogenomics. Computer Science > Information Theory Examples include recovering sparse or group-sparse vectors, low-rank matrices, and the sum of sparse and low-rank matrices, among others. 9 Ruediger Borsdorf, Structured Matrix Nearness Problems: Theory and Algorithms, Ph.D. In this book the authors try to bridge the gap between the treatments of matrix theory and linear algebra. Tara A Gianoulis, Ashish Agarwal, Michael Although methods are available for integrating more than three matrices when all share the same index variable (see discussion in [4]), how to integrate features when they do not all share the same index remains an open question. CRIT in theory is not limited to three levels. Thesis, University of Manchester, 2012. Zhou, Uniform asymptotics for polynomials orthogonal with respect to varying exponential weights and applications to universality questions in random matrix theory, Comm. We suggest that cross patterns .. The conventional formulation of superstring theory is valid only when the interaction between strings is weak, so it is not suitable for application to real physical phenomena.