We give an algorithm for learning a permutation on-line. The algorithm maintains its uncertainty about the target permutation as a doubly stochastic matrix. This matrix is updated by multiplying the current matrix entries by exponential factors which destroy the doubly stochastic property of the matrix, and an iterative procedure is needed to renormalize the rows and columns. Even though the re...

We give an algorithm for learning a permutation on-line. The algorithm maintains its uncertainty about the target permutation as a doubly stochastic matrix. This matrix is updated by multiplying the current matrix entries by exponential factors. These factors destroy the doubly stochastic property of the matrix and an iterative procedure is needed to re-normalize the rows and columns. Even thou...

In this paper we focus on the issue of normalization of the affinity matrix in spectral clustering. We show that the difference between N-cuts and Ratio-cuts is in the error measure being used (relative-entropy versus L1 norm) in finding the closest doubly-stochastic matrix to the input affinity matrix. We then develop a scheme for finding the optimal, under Frobenius norm, doubly-stochastic ap...

A Steinhaus matrix is a binary square matrix of size n which is symmetric, with diagonal of zeros, and whose upper-triangular coefficients satisfy ai,j = ai−1,j−1+ai−1,j for all 2 6 i < j 6 n. Steinhaus matrices are determined by their first row. A Steinhaus graph is a simple graph whose adjacency matrix is a Steinhaus matrix. We give a short new proof of a theorem, due to Dymacek, which states...

Abstract We provide a decomposition that is sufficient in showing when symmetric tridiagonal matrix A A completely positive. Our can be applied to wide range of matrices. give alternate proofs for number related results found the literature simple, straightforward manner. show cp-rank any positive irreducible doubly st...

In this paper, we develop a regularization framework for image deblurring based on a new definition of the normalized graph Laplacian. We apply a fast scaling algorithm to the kernel similarity matrix to derive the symmetric, doubly stochastic filtering matrix from which the normalized Laplacian matrix is built. We use this new definition of the Laplacian to construct a cost function consisting...

In this work we apply Dykstra’s alternating projection algorithm for minimizing ‖AX − B‖ where ‖ · ‖ is the Frobenius norm and A ∈ Rm×n, B ∈ Rm×n and X ∈ Rn×n are doubly symmetric positive definite matrices with entries within prescribed intervals. We first solve the constrained least-squares matrix problem by using the special structure properties of doubly symmetric matrices, and then use the...

In this paper we propose a concavely regularized convex relaxation based graph matching algorithm. The graph matching problem is firstly formulated as a constrained convex quadratic program by relaxing the feasible set from the permutation matrices to doubly stochastic matrices. To gradually push the doubly stochastic matrix back to be a permutation one, an objective function is constructed by ...

This paper considers certain comparison techniques involving Markov chains with transition matrices Pa = aI+(1?a)P where P is the transition matrix of a doubly stochastic Markov chain. This paper provides upper bounds for how far the Markov chain with transition matrix Pb is from uniformly distributed after n steps. These upper bounds involve how far the Markov chain with transition matrix Pa i...