Consider the problem of estimating the entries of a large matrix, when the observed entries
are noisy versions of a small random fraction of the original entries. This problem has …

Multidimensionalscaling (MDS) isatechniquefortheanalysisofsimilarity or dissimilarity data
on a set of objects. Such data may be intercorrelations of test items, ratings of similarity on …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

As the amount of information recorded and stored electronically grows ever larger, it
becomes increasingly useful, if not essential, to develop better and more efficient ways to …

In this paper, we develop a theory of matrix completion for the extreme case of noisy 1-bit
observations. Instead of observing a subset of the real-valued entries of a matrix M, we …

The author describes the construction of a scale to measure self-concepts, person concepts,
and product concepts. Multivariate analysis and multidimensional scaling procedures are …

Publisher Summary Multidimensional scaling (MDS) is a versatile technique for
understanding and displaying the structure of multivariate data. This technique has seen …

Given a set of dissimilarities data between n objects, multidimensional scaling is the
problem of reconstructing a geometrical pattern of these objects, using n points, so that …

We consider in this paper the problem of noisy 1-bit matrix completion under a general non-
uniform sampling distribution using the max-norm as a convex relaxation for the rank. A max …

Sustainable food packaging alternatives represent an ever-expanding trend on
supermarkets' shelves. Despite the technological efforts, a higher sustainability level often …