Abstract: Multidimensional scaling (MDS) represents objects as points in an Euclidean space so that the perceived distances between points can reflect similarity (or dissimilarity) between objects. To be practical, the dimension of the projected space usually is kept as low as possible. Thus, it is unavoidable that part of the information in the original proximity matrix will be lost in the MDS...

Overview From a non-technical point of view, the purpose of multidimensional scaling (MDS) is to provide a visual representation of the pattern of proximities (i.e., similarities or distances) among a set of objects. For example, given a matrix of perceived similarities between various brands of air fresheners, MDS plots the brands on a map such that those brands that are perceived to be very s...

Multidimensional scaling (MDS) is a collection of data analytic techniques for constructing configurations of points from dissimilarity information about interpoint distances. Classsical MDS assumes a fixed matrix of dissimilarities. However, in some applications, e.g., the problem of inferring 3-dimensional molecular structure from bounds on interatomic distances, the dissimilarities are free ...

A good linear diffusion layer is a prerequisite in the design of block ciphers. Usually it is obtained by combining matrices with optimal diffusion property over the Sbox alphabet. These matrices are constructed either directly using some algebraic properties or by enumerating a search space, testing the optimal diffusion property for every element. For implementation purposes, two types of str...

Previous algorithms for multidimensional scaling, or MDS, aim for scalable performance as the number of points to lay out increases. However, they either assume that the distance function is cheap to compute, and perform poorly when the distance function is costly, or they leave the precise number of distances to compute as a manual tuning parameter. We present Glint, an MDS algorithm framework...

Multidimensional scaling (MDS) is a class of methods used to find a low-dimensional representation of a set of points given a matrix of pairwise distances between them. Problems of this kind arise in various applications, from dimensionality reduction of image manifolds to psychology and statistics. In many of these applications, efficient and accurate solution of an MDS problem is required. In...

In statistical NLP, Semantic Vector Spaces (SVS) are the standard technique for the automatic modeling of lexical semantics. However, it is largely unclear how these black-box techniques exactly capture word meaning. To explore the way an SVS structures the individual occurrences of words, we use a non-parametric MDS solution of a token-by-token similarity matrix. The MDS solution is visualized...

Array codes have been widely used in communication and storage systems. To reduce computational complexity, one important property of the array codes is that only XOR operation is used in the encoding and decoding process. In this work, we present a novel family of maximal-distance separable (MDS) array codes based on Cauchy matrix, which can correct up to any number of failures. We also propos...

myelodysplastic syndromes (mdss) are a clonal bone marrow (bm) disease characterized by ineffective hematopoiesis, dysplastic maturation and progression to acute myeloid leukemia (aml). methylation silencing of hrk has been found in several human malignancies. in this study, we explored the association of hrk methylation status with its expression, clinical parameters and mds subtypes in mds pa...

Multidimensional scaling (MDS) methods are designed to establish a one-to-one correspondence of input-output relationships. While the input may be given as high-dimensional data items or as adjacency matrix characterizing data relations, the output space is usually chosen as low-dimensional Euclidean, ready for visualization. MDSLocalize, an existing method, is reformulated in terms of Sanger’s...