ON SCALING FROM INCOMPLETE PAIRED COMPARISON MATRIX

Monte Carlo Analysis of Incomplete Paired-Comparison Experiments

The use of paired-comparison psychophysical experiments is an important technique that is used widely in imaging studies. It is sometimes difficult to compare every stimulus with every other; the number of paired comparisons for n stimuli becomes prohibitive for large values of n. Thus, experiments are often designed by missing some pairs. However, the effect on the accuracy of the estimations ...

Models Estimation for Incomplete Paired-Comparison Experiments Based on Least-Squares Solution

Incomplete paired comparison is an important scaling technique in vision science since the total number of paired comparisons for n stimuli is n(n-1)/2 which becomes prohibitive for large values of n. However, the experimental designer often struggles with questions such as what is the smallest limit for the proportion of paired comparisons included that will still allow reliable estimations of...

On Complexity of Matrix Scaling

The Line Sum Scaling problem for a nonnegative matrix A is to find positive definite diagonal matrices Y , Z which result in prescribed row and column sums of the scaled matrix Y AZ. The Matrix Balancing problem for a nonnegative square matrix A is to find a positive definite diagonal matrix X such that the row sums in the scaled matrix XAX are equal to the corresponding column sums. We demonst...

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data