Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pai...

Dimension reduction techniques are widely used for the analysis and visualization of complex sets of data. This paper compares two nonlinear projection methods: Isomap and Curvilinear Distance Analysis. Contrarily to the traditional linear PCA, these methods work like multidimensional scaling, by reproducing in the projection space the pairwise distances measured in the data space. They differ ...

This paper analyzes the Portuguese short-run business cycles over the last 150 years and presents the multidimensional scaling (MDS) for visualizing the results. The analytical and numerical assessment of this long-run perspective reveals periods with close connections between the macroeconomic variables related to government accounts equilibrium, balance of payments equilibrium, and economic g...

Recently, a family of methods have been proposed for large scale ad hoc sensor networks that estimate the approximate positions of sensor nodes in the network. In the paper, we first investigate some situations that most existing sensor positioning methods fail to perform well. An example of such situations is that the topology of a sensor network is anisotropic. Then, we study the multidimensi...

Listeners judged the dissimilarity of pairs of synthesized nasal voices that varied on 3 dimensions. Separate nonmetric multidimensional scaling (MDS) solutions were calculated for each listener and the group. Similar 3-dimensional solutions were derived for the group and each of the listeners, with the group MDS solution accounting for 83% of the total variance in listeners' judgments. Dimensi...

Motivation Recent experiments have provided Hi-C data at resolution as high as 1 kbp. However, 3D structural inference from high-resolution Hi-C datasets is often computationally unfeasible using existing methods. Results We have developed miniMDS, an approximation of multidimensional scaling (MDS) that partitions a Hi-C dataset, performs high-resolution MDS separately on each partition, and ...

Mathematical models for the prediction of company failure are by now well established . Most of the work on multivariate modelling of distress prediction attempts to obtain a score that gives the failure probability of a company. A data set of 66 Spanish banks , 29 of which failed , is used to show that multidimensional scaling (MDS) techniques can be of use to produce simple tools for the anal...

A three-dimensional (3D) theoretical morphospace of gomphonemoid and cymbelloid diatoms was skeletonized using concepts from extended Reeb graph analysis and Morse theory. The resultant skeleton tree was matched to a cladogram of the same group of related taxa using adjacency matrices of the trees and ordinated with multidimensional scaling (MDS) of leaf nodes. From this, an unweighted path mat...

Although factor analysis is the most commonly-used method for examining the structure of cognitive variable interrelations, multidimensional scaling (MDS) can provide visual representations highlighting the continuous nature of interrelations among variables. Using data (N = 8,813; ages 17-97 years) aggregated across 38 separate studies, MDS was applied to 16 cognitive variables representative ...

Landmark Multidimensional Scaling (LMDS) The O(N) memory complexity of cMDS makes it an impractical choice when N is the number of pixels. Randomized approaches sample rows of the distance matrix to build approximate representatives of the entire matrix [4]. Depending on the coherence of the right singular vectors of D, it has been shown [2] that the Nyström approximation is the original matrix...