We propose a general framework for nonasymptotic covariance matrix estimation making use of concentration inequality-based confidence sets. specify this the large sparse matrices through incorporation past thresholding estimators with key emphasis on support recovery. This technique goes beyond results by allowing wide range distributional assumptions merely sub-Gaussian tails. methodology can ...

in this paper, the concept of k-regular fuzzy matrix as a general- ization of regular matrix is introduced and some basic properties of a k-regular fuzzy matrix are derived. this leads to the characterization of a matrix for which the regularity index and the index are identical. further the relation between regular, k-regular and regularity of powers of fuzzy matrices are dis- cussed.

The computational and/or communication constraints associated with processing large-scale data sets using support vector machines (SVM) in contexts such as distributed networking systems are often prohibitively high, resulting in practitioners of SVM learning algorithms having to apply the algorithm on approximate versions of the kernel matrix induced by a certain degree of data reduction. In t...

Let $a, b, k,in K$ and $u, v in U(K)$. We show for any idempotent $ein K$, $(a 0|b 0)$ is e-clean iff $(a 0|u(vb + ka) 0)$ is e-clean and if $(a 0|b 0)$ is 0-clean, $(ua 0|u(vb + ka) 0)$ is too.

In previous work we have developed procedures to analyse, compute with and reason about abstract matrices, that is, matrices represented with symbolic dimensions and with a mixture of terms and ellipsis symbols to describe their structure. A central component in this are the so-called “support functions”, which enable the representation of abstract matrices in closed forms. A key issue in makin...

Strongly quadrangular matrices have been introduced in the study of the combinatorial properties of unitary matrices. It is known that if a (0, 1)-matrix supports a unitary then it is strongly quadrangular. However, the converse is not necessarily true. In this paper, we fully classify strongly quadrangular matrices up to degree 5. We prove that the smallest strongly quadrangular matrices which...