Localization of sensors nodes is a key and fundamental issue in wireless sensor networks due to random deployment. In this paper, we propose a tree based clustering (TBC) multidimensional scaling algorithm for wireless sensor networks with the purpose of overcoming the shortage of classical MDS algorithms in its localization accuracy and computing complexity. Clustering is adopted to degrade th...

Abstract. Random Forest (RF) is a powerful ensemble method for classification and regression tasks. It consists of decision trees set. Although, a single tree is well interpretable for human, the ensemble of trees is a black-box model. The popular technique to look inside the RF model is to visualize a RF proximity matrix obtained on data samples with Multidimensional Scaling (MDS) method. Here...

Isomap [4] is a manifold learning algorithm, which extends classical multidimensional scaling (MDS) by considering approximate geodesic distance instead of Euclidean distance. The approximate geodesic distance matrix can be interpreted as a kernel matrix, which implies that Isomap can be solved by a kernel eigenvalue problem. However, the geodesic distance kernel matrix is not guaranteed to be ...

Recently, in response to industrial demand to develop faster and cheaper methods of descriptive analysis, several methods have been offered as alternatives to conventional profiling. We present three of these methods: sorting tasks, projective mapping, and flash profiling along with a new statistical method to analyze them. This new statistical method, called DISTATIS is a generalization of cla...

-Identifying the underlying decision criteria used by operators to classi&y system state and revealing the way in which that information is internally represented by individual operators is one of the major challenges facing designers of decision aids for process plants. This researqh describes the use of multidimensional scaling (MDS) to probe the structure and composition of the mental models...

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 ...

In this paper we approach the problem of calibrating topologically a discrete camera mounted on a robotic pan-tilt basis. In particular this work is focused in choosing the coordinate system of the camera consistently with the basis. The topological calibrating methodology of the sensor is based on multidimensional scaling (MDS). The choice of the coordinate system is based in performing known ...

Experiments were conducted in which Ss made classification, recognition, and similarity judgments for 34 schematic faces. A multidimensional scaling (MDS) solution for the faces was derived on the basis of the similarity judgments. This MDS solution was then used in conjunction with an exemplar-similarity model to accurately predict Ss' classification and recognition judgments. Evidence was pro...

We assessed the qualitative accord between several automatic face recognition algorithms and human perceivers. By comparing modeland human-generated measures of the similarity between pairs of faces, we were able to evaluate the suitability of the algorithms as models of human face recognition. Multidimensional scaling (MDS) was used to create a spatial representation of the subject response pa...

This study explored the attributes of languages to which listeners attend, using magnitude estimation and multidimensional scaling techniques. In magnitude estimation. lislcners assign any numerical value to a set of stimuli. In response to the question: Ilow similar is this language to English’? fifty college students assigned numerical values to spoken samples of foreign languages. The langua...