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<p class="MsoPlainText"><o:p> </o:p></p>
<p class="MsoPlainText"><span lang="EN-US">CALL FOR PAPER <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">(Apologies for receiving multiple copies)<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">Visual Analytics using Multidimensional Projections (VAMP 2013) Workshop
<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">co-located with EuroVis 2013 conference
<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">June 19, 2013 <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">Leipzig, Germany<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">WEBSITE: </span><a href="http://homepage.tudelft.nl/19j49/eurovis2013"><span lang="EN-US">http://homepage.tudelft.nl/19j49/eurovis2013</span></a>
<span lang="EN-US"><o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">We solicit submissions for oral or poster presentation at the First International Workshop on Visual Analytics using Multidimensional Projections (VAMP), to be held on June 19, 2013, in Leipzig, Germany, in conjunction
with the EuroVis 2013 conference. The workshop will bring together researchers and practitioners from academia and industry to discuss the latest developments in the cross-section of information visualization, machine learning, and graph drawing.
<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">Best papers will be selected for publication of an extended version in a special issue of the Neurocomputing journal (Elsevier).<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">CONTEXT<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">Dimensionality reduction is an active area in machine learning. New techniques have been proposed for more than 50 years, for instance, principal component analysis, classical scaling, isomap, probabilistic latent
trait models, stochastic neighbor embedding, and neighborhood retrieval visualization. These techniques facilitate the visualization of high-dimensional data by representing data instances as points in a two-dimensional space in such a way that similar instances
are modeled by nearby points and dissimilar instances are modeled by distant points.<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">Although many papers on these so-called “embedding” techniques are published every year, which aim to improve visual representations of high-dimensional data, it appears that these techniques have not gained popularity
in the EuroVis community due to the inherent complexity of their interpretation.<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">TOPICS<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">At the cross-section of information visualization, machine learning, and graph drawing, this workshop will focus on issues that embedding techniques should address to bridge the gap with the information visualization
community. Below is a (non-exhaustive) list of topics on which we solicit submissions for the workshop:<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US" style="font-family:"Cambria Math","serif"">⎯</span><span lang="EN-US"> Stability: Nonlinear embedding techniques are more efficient at preserving similarities than linear ones. However, non-linearities generate local
optima as a result of which different initializations lead to different representations of the same data. The differences between these embeddings of the same data create confusion for the analyst, who is unable to grasp the common facts across the different
visualizations. How can we design efficient and stable nonlinear embeddings?<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US" style="font-family:"Cambria Math","serif"">⎯</span><span lang="EN-US"> Embedding of dynamic data: Embedding usually projects all the data at once; when new data arrive, how can we embed these data without modifying
the current embedding too much?<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US" style="font-family:"Cambria Math","serif"">⎯</span><span lang="EN-US"> Multiple methods: Each embedding algorithm necessarily comes with its own set of built-in underlying assumptions, and knowledge of these assumptions
is often helpful in making sense of the visual output. How can we design black-box visualization methods that demand less understanding of underlying assumptions from the side of the analyst?<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US" style="font-family:"Cambria Math","serif"">⎯</span><span lang="EN-US"> Evaluation and subjectivity: Visual interpretation is inherently subjective. How can we help analysts to verify whether an eye-catching pattern
is real/essential or whether it just happens to be an artefact?<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US" style="font-family:"Cambria Math","serif"">⎯</span><span lang="EN-US"> Inference and interactions: Nonlinear embedding techniques produce points clouds in which the axes have no meaning and pairwise distances are approximations
which may have many artefacts. What kinds of analytical tasks can be performed with such embeddings? How can we better convey the meaning of the embeddings to analysts?<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US" style="font-family:"Cambria Math","serif"">⎯</span><span lang="EN-US"> Feedback: The human eye is excellent at visual analysis, and can identify regularities and anomalous data even without having to define an algorithm.
How can we make use of this ability to enhance the predictive performance of machine learning and embedding techniques?<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US" style="font-family:"Cambria Math","serif"">⎯</span><span lang="EN-US"> Input data: Currently, the input data in embedding techniques typically comprises high-dimensional feature vectors or pairwise distance between
objects. However, this is not always the kind of data that analysts encounter in practice. How can embeddings be constructed based on partial similarity rankings, associations or co-occurences of objects, heterogeneous data, data with missing values, relations
between objects, structured objects, etc.?<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US" style="font-family:"Cambria Math","serif"">⎯</span><span lang="EN-US"> Optimizing embeddings for visual analysis: nonlinear embeddings are found by optimizing mathematical goodness-of-fit measures. Instead of using
off-the-shelf embedding methods, can the measures and methods be designed so that the optimized embeddings will be good for carrying out concrete low-level or high-level analysis tasks from the visualization?<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">SUBMISSIONS<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">Submissions should report new (unpublished) research results or ongoing research.<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">Short papers should be 4 pages (at most), excluding references, and 5 pages (at most), in total.
<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">Papers should be formatted in the style of EuroVis Short Papers.
<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">Papers must be in English and must be submitted as PDF files.
<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">Electronic submission is to be done following the EuroVis short papers guidelines.<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">At least one author of each accepted submission will be expected to attend and present their findings at the workshop.<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">All accepted papers will be published on the workshop website (upon authors' acceptance).<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">Authors of best papers will be selected to submit an extended version to a special issue of the Neurocomputing journal (Elsevier).<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">IMPORTANT DATES<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">- Paper submission: 23:59 UTC/GMT, Friday, March 8, 2013.<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">- VAMP Workshop: Wednesday, June 19, 2013.<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">PROGRAM COMMITTEE<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">Michael Aupetit, CEA LIST<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="EN-US">Laurens van der Maaten, Delft University of Technology Daniel Keim, University of Konstanz Jean-Daniel Fekete, INRIA Jaakko Peltonen, Aalto University Ata Kaban, University of Birmingham John Lee, Universite catholique
de Louvain Samuel Kaski, Aalto University Frank-Michael Schleif, University of Bielefeld Leishi Zhang, University of Konstanz Michel Verleysen, Universite Catholique de Louvain Nicolas Heulot, CEA LIST<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
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