3WaySym-Scal: three-way symbolic multidimensional scaling

Multidimensional scaling aims at reconstructing dissimilarities between pairs of objects by distances in a low dimensional space.However, in some cases the dissimilarity itself is not known, but the range, or a histogram of the dissimilarities is given. This type of data fall in the wider class of symbolic data (see Bock and Diday (2000)). We model three-way two-mode data consisting of an interval of dissimilarities for each object pair from each of K sources by a set of intervals of the distances defined as the minimum and maximum distance between two sets of embedded rectangles representing the objects. In this paper, we provide a new algorithm called 3WaySym-Scal using iterative majorization, that is based on an algorithm, I-Scal developed for the two-way case where the dissimilarities are given by a range of values ie an interval (see Groenen et al. (2006)).The advantage of iterative majorization is that each iteration is guaranteed to improve the solution until no improvement is possible. We present the results on an empirical data set on synthetic musical tones.2WaySym-Scal;interval data;multidimensional scaling;symbolic data analysis;three-way data

SymScal: symbolic multidimensional scaling of interval dissimilarities

Multidimensional scaling aims at reconstructing dissimilaritiesbetween pairs of objects by distances in a low dimensional space.However, in some cases the dissimilarity itself is unknown, but therange of the dissimilarity is given. Such fuzzy data fall in thewider class of symbolic data (Bock and Diday, 2000).Denoeux and Masson (2000) have proposed to model an intervaldissimilarity by a range of the distance defined as the minimum andmaximum distance between two rectangles representing the objects. Inthis paper, we provide a new algorithm called SymScal that is basedon iterative majorization. The advantage is that each iteration isguaranteed to improve the solution until no improvement is possible.In a simulation study, we investigate the quality of thisalgorithm. We discuss the use of SymScal on empirical dissimilarityintervals of sounds.iterative majorization;multidimensional scaling;symbolic data analysis;distance smoothing

Perceiving environmental structure from optical motion

Generally speaking, one of the most important sources of optical information about environmental structure is known to be the deforming optical patterns produced by the movements of the observer (pilot) or environmental objects. As an observer moves through a rigid environment, the projected optical patterns of environmental objects are systematically transformed according to their orientations and positions in 3D space relative to those of the observer. The detailed characteristics of these deforming optical patterns carry information about the 3D structure of the objects and about their locations and orientations relative to those of the observer. The specific geometrical properties of moving images that may constitute visually detected information about the shapes and locations of environmental objects is examined

A new and efficient intelligent collaboration scheme for fashion design

Technology-mediated collaboration process has been extensively studied for over a decade. Most applications with collaboration concepts reported in the literature focus on enhancing efficiency and effectiveness of the decision-making processes in objective and well-structured workflows. However, relatively few previous studies have investigated the applications of collaboration schemes to problems with subjective and unstructured nature. In this paper, we explore a new intelligent collaboration scheme for fashion design which, by nature, relies heavily on human judgment and creativity. Techniques such as multicriteria decision making, fuzzy logic, and artificial neural network (ANN) models are employed. Industrial data sets are used for the analysis. Our experimental results suggest that the proposed scheme exhibits significant improvement over the traditional method in terms of the time–cost effectiveness, and a company interview with design professionals has confirmed its effectiveness and significance

Developing Conceptual Land Grade Model for Bench Mark Lease Price Determination using Fuzzy Analytical Hierarchy Process and GIS Approach: A Case of Woldiya City, Ethiopia

Land grading plays a crucial role to develop appropriate benchmark lease prices. An efficient land grading system brings transparent and clear land marketing system.  However, the existing land grading system of urban centers of Ethiopia has not been very successful in allowing the urban centers to benefit from the advantages of having an efficient land grading system. The major reasons for those limitations emanates from three main factors. The first reason is due to the fact that some major factors that have paramount importance for developing urban land grading had been left out. The second limitation is related to a methodological approach how land grade is prepared. In Ethiopia land grading is developed in the context of traditional urban land uses models. Yet, the notion of mono centric city concept (Burger’s 1925 model) is dominant. However, currently this model does not reasonably represent real land value patterns in urban Ethiopia. Thirdly, in Ethiopia, the conventional method used to prepare land grade map is manual and labor intensive due to this limitations most municipals are not in position to prepare predictable and flexible land grade map.Taking this gap into account, the objective of this research was to develop a predictable and flexible conceptual land grading model for improved land management system in urban areas. In this study three -stage methodologies were used. The first stage was focused in identifying influential factors that are used as input to develop conceptual land grading model. The second was about determining preference weight of criteria using FAHP method. The third method was aggregating the factor maps in arc GIS10.2 (map algebra) so as to create prototype land grading map. The developed conceptual model was found to be effective and has improved information gain compared to previous works. The study concluded that GIS  approach  is not only facilitate the organization and management of digital data layers but it also enable municipals to take full advantage of location information contained in databases that can support spatial decision  making in land grading. The study also confirmed that FAHP model allows decision makers to give interval judgments, which can capture a human‘s appraisal of ambiguity when complex multi-attribute decision making approaches like land grade are involved. Keywords: Fuzzy; GIS; land grade; valuation; benchmark price

3WaySym-Scal: three-way symbolic multidimensional scaling

Multidimensional scaling aims at reconstructing dissimilarities between pairs of objects by distances in a low dimensional space. However, in some cases the dissimilarity itself is not known, but the range, or a histogram of the dissimilarities is given. This type of data fall in the wider class of symbolic data (see Bock and Diday (2000)). We model three-way two-mode data consisting of an interval of dissimilarities for each object pair from each of K sources by a set of intervals of the distances defined as the minimum and maximum distance between two sets of embedded rectangles representing the objects. In this paper, we provide a new algorithm called 3WaySym-Scal using iterative majorization, that is based on an algorithm, I-Scal developed for the two-way case where the dissimilarities are given by a range of values ie an interval (see Groenen et al. (2006)). The advantage of iterative majorization is that each iteration is guaranteed to improve the solution until no improvement is possible. We present the results on an empirical data set on synthetic musical tones

The Past, Present, and Future of Multidimensional Scaling

Multidimensional scaling (MDS) has established itself as a standard tool for statisticians and applied researchers. Its success is due to its simple and easily interpretable representation of potentially complex structural data. These data are typically embedded into a 2-dimensional map, where the objects of interest (items, attributes, stimuli, respondents, etc.) correspond to points such that those that are near to each other are empirically similar, and those that are far apart are different. In this paper, we pay tribute to several important developers of MDS and give a subjective overview of milestones in MDS developments. We also discuss the present situation of MDS and give a brief outlook on its future

SymScal: symbolic multidimensional scaling of interval dissimilarities

Multidimensional scaling aims at reconstructing dissimilarities between pairs of objects by distances in a low dimensional space. However, in some cases the dissimilarity itself is unknown, but the range of the dissimilarity is given. Such fuzzy data fall in the wider class of symbolic data (Bock and Diday, 2000). Denoeux and Masson (2000) have proposed to model an interval dissimilarity by a range of the distance defined as the minimum and maximum distance between two rectangles representing the objects. In this paper, we provide a new algorithm called SymScal that is based on iterative majorization. The advantage is that each iteration is guaranteed to improve the solution until no improvement is possible. In a simulation study, we investigate the quality of this algorithm. We discuss the use of SymScal on empirical dissimilarity intervals of sounds