Visualising Changes in Fund Manager Holdings in Two and a Half-Dimensions

2004 ◽  
Vol 3 (4) ◽  
pp. 227-244 ◽  
Author(s):  
Tim Dwyer ◽  
David R. Gallagher
Keyword(s):  
High Dimensional ◽  
Prototype System ◽  
Large Set ◽  
Data Set ◽  
Fund Managers ◽  
Portfolio Holdings ◽  
The Third ◽  
Stock Portfolios ◽  
Third Dimension ◽  
Dimensional Mapping

We explore a multiple view, or overview and detail, method for visualising a high-dimensional portfolio holdings data set with attributes that change over time. The method employs techniques from multidimensional scaling and graph visualisation to find a two-dimensional mapping for high-dimensional data. In both the overview and detail views, time is mapped to the third dimension providing a two and a half-dimensional view of changes in the data. We demonstrate the utility of the paradigm with a prototype system for visualisation of movements within a large set of UK fund managers’ stock portfolios.

scholarly journals Beyond the Third Dimension: Visualizing High-Dimensional Data with Projections

2016 ◽  
Vol 18 (5) ◽  
pp. 98-107 ◽  
Author(s):  
Renato R.O. da Silva ◽  
Paulo E. Rauber ◽  
Alexandru C. Telea
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
The Third ◽  
Third Dimension

scholarly journals How to visualize high-dimensional data: a roadmap

2020 ◽  
Vol Special issue on... ◽  
Author(s):  
Hermann Moisl
Keyword(s):  
Cluster Analysis ◽  
High Dimensional Data ◽  
Latent Structure ◽  
High Dimensional ◽  
Graphical Methods ◽  
Data Set ◽  
The Third ◽  
Historical Text ◽  
International Audience ◽  
And Cluster Analysis

International audience Discovery of the chronological or geographical distribution of collections of historical text can be more reliable when based on multivariate rather than on univariate data because multivariate data provide a more complete description. Where the data are high-dimensional, however, their complexity can defy analysis using traditional philological methods. The first step in dealing with such data is to visualize it using graphical methods in order to identify any latent structure. If found, such structure facilitates formulation of hypotheses which can be tested using a range of mathematical and statistical methods. Where, however, the dimensionality is greater than 3, direct graphical investigation is impossible. The present discussion presents a roadmap of how this obstacle can be overcome, and is in three main parts: the first part presents some fundamental data concepts, the second describes an example corpus and a high-dimensional data set derived from it, and the third outlines two approaches to visualization of that data set: dimensionality reduction and cluster analysis.

A New Outlier Detection Algorithms Based on Markov Chain

2011 ◽  
Vol 366 ◽  
pp. 456-459 ◽  
Author(s):  
Jun Yang ◽  
Ying Long Wang
Keyword(s):  
Markov Chain ◽  
Outlier Detection ◽  
High Dimensional Data ◽  
Weighted Graph ◽  
Real Data ◽  
Curse Of Dimensionality ◽  
High Dimensional ◽  
Large Set ◽  
Data Set ◽  
Novel Approach

Detecting outliers in a large set of data objects is a major data mining task aiming at finding different mechanisms responsible for different groups of objects in a data set. In high-dimensional data, these approaches are bound to deteriorate due to the notorious “curse of dimensionality”. In this paper, we propose a novel approach named ODMC (Outlier Detection Based On Markov Chain)，the effects of the “curse of dimensionality” are alleviated compared to purely distance-based approaches. A main advantage of our new approach is that our method is to use a major feature of an undirected weighted graph to calculate the outlier degree of each node, In a thorough experimental evaluation, we compare ODMC to the ABOD and FindFPOF for various artificial and real data set and show ODMC to perform especially well on high-dimensional data.

Molecular Views of Ice-Embedded Lumbricus Terrestris Erythrocruorin Obtained By Invariant Classification

1990 ◽  
Vol 48 (1) ◽  
pp. 450-451
Author(s):  
Michael schatz ◽  
Joachim Jäger ◽  
Marin van Heel
Keyword(s):  
Image Interpretation ◽  
Lumbricus Terrestris ◽  
Reference Image ◽  
Large Set ◽  
Multivariate Statistical ◽  
Data Set ◽  
Low Contrast ◽  
Negative Stain ◽  
Reference Images ◽  
Statistical Resolution

Lumbricus terrestris erythrocruorin is a giant oxygen-transporting macromolecule in the blood of the common earth worm (worm "hemoglobin"). In our current study, we use specimens (kindly provided by Drs W.E. Royer and W.A. Hendrickson) embedded in vitreous ice (1) to avoid artefacts encountered with the negative stain preparation technigue used in previous studies (2-4).Although the molecular structure is well preserved in vitreous ice, the low contrast and high noise level in the micrographs represent a serious problem in image interpretation. Moreover, the molecules can exhibit many different orientations relative to the object plane of the microscope in this type of preparation. Existing techniques of analysis requiring alignment of the molecular views relative to one or more reference images often thus yield unsatisfactory results.We use a new method in which first rotation-, translation- and mirror invariant functions (5) are derived from the large set of input images, which functions are subsequently classified automatically using multivariate statistical techniques (6). The different molecular views in the data set can therewith be found unbiasedly (5). Within each class, all images are aligned relative to that member of the class which contributes least to the classes′ internal variance (6). This reference image is thus the most typical member of the class. Finally the aligned images from each class are averaged resulting in molecular views with enhanced statistical resolution.

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