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<p class="MsoPlainText"><b><span lang="EN-US">Call for Papers<o:p></o:p></span></b></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>
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<p class="MsoNormal"><b><u><span lang="EN-US" style="font-family:"Trebuchet MS","sans-serif";background:white">Visual Analytics using Multidimensional Projections Workshop (co-located with EuroVis 2013)<o:p></o:p></span></u></b></p>
<p class="MsoNormal"><span style="font-family:"Trebuchet MS","sans-serif";background:white">June 19, 2013<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Trebuchet MS","sans-serif";background:white">Leipzig, Germany<o:p></o:p></span></p>
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<p class="MsoNormal">Website: <a href="http://homepage.tudelft.nl/19j49/eurovis2013">
http://homepage.tudelft.nl/19j49/eurovis2013</a><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-family:"Trebuchet MS","sans-serif";background:white">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. 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. We are planning a special issue for the best papers of the workshop.</span><o:p></o:p></p>
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<p class="MsoNormal"><b>Call for Papers</b><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-family:"Trebuchet MS","sans-serif";background:white">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 modelled by distant points.</span><span style="font-family:"Trebuchet MS","sans-serif""><br>
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<span style="background:white">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.</span><br>
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<span style="background:white">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:</span><br>
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</span><span style="font-family:"Cambria Math","serif";background:white">⎯</span><span style="font-family:"Trebuchet MS","sans-serif";background:white"> <b>Stability:</b> 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?</span><span style="font-family:"Trebuchet MS","sans-serif""><br>
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</span><span style="font-family:"Cambria Math","serif";background:white">⎯</span><span style="font-family:"Trebuchet MS","sans-serif";background:white"> <b>Embedding of dynamic data:</b> 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?</span><span style="font-family:"Trebuchet MS","sans-serif""><br>
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</span><span style="font-family:"Cambria Math","serif";background:white">⎯</span><span style="font-family:"Trebuchet MS","sans-serif";background:white"> <b>Multiple methods:</b> 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?</span><span style="font-family:"Trebuchet MS","sans-serif""><br>
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</span><span style="font-family:"Cambria Math","serif";background:white">⎯</span><span style="font-family:"Trebuchet MS","sans-serif";background:white"> <b>Evaluation and subjectivity:</b> 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?</span><span style="font-family:"Trebuchet MS","sans-serif""><br>
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</span><span style="font-family:"Cambria Math","serif";background:white">⎯</span><span style="font-family:"Trebuchet MS","sans-serif";background:white"> <b>Inference and interactions:</b> 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?</span><span style="font-family:"Trebuchet MS","sans-serif""><br>
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</span><span style="font-family:"Cambria Math","serif";background:white">⎯</span><span style="font-family:"Trebuchet MS","sans-serif";background:white"> <b>Feedback:</b> 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?</span><span style="font-family:"Trebuchet MS","sans-serif""><br>
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</span><span style="font-family:"Cambria Math","serif";background:white">⎯</span><span style="font-family:"Trebuchet MS","sans-serif";background:white"> <b>Input data:</b> 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.?</span><span style="font-family:"Trebuchet MS","sans-serif""><br>
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</span><span style="font-family:"Cambria Math","serif";background:white">⎯</span><span style="font-family:"Trebuchet MS","sans-serif";background:white"> <b>Optimizing embeddings for visual analysis:</b> 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?</span><o:p></o:p></p>
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<p class="MsoNormal"><b><span style="font-family:"Trebuchet MS","sans-serif";background:white">Submissions</span></b><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-family:"Trebuchet MS","sans-serif";background:white">Submissions should report new (unpublished) research results or ongoing research. Papers should be 4 pages (at most), excluding references, and 5 pages (at most), in
total. Papers should be formatted in the style of EuroVis Short Papers. Papers must be in English and must be submitted as PDF files. Papers will be published on the workshop website. We are planning a special issue for the best papers of the workshop.</span><span style="font-family:"Trebuchet MS","sans-serif""><br>
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<span style="background:white">At least one author of each accepted submission will be expected to attend and present their findings at the workshop.</span><br>
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<span style="background:white">Papers should be submitted electronically no later than 23:59 UTC/GMT, Friday, March 8, 2013.</span></span><o:p></o:p></p>
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<p class="MsoNormal"><b><span style="font-family:"Trebuchet MS","sans-serif"">Program committee</span></b><o:p></o:p></p>
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<p class="MsoNormal" style="line-height:15.0pt"><span style="font-family:"Trebuchet MS","sans-serif"">Michael Aupetit, CEA LIST<o:p></o:p></span></p>
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<p class="MsoNormal" style="line-height:15.0pt"><span style="font-family:"Trebuchet MS","sans-serif"">Laurens van der Maaten, Delft University of Technology<o:p></o:p></span></p>
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<p class="MsoNormal" style="line-height:15.0pt"><span style="font-family:"Trebuchet MS","sans-serif"">Daniel Keim, University of Konstanz<o:p></o:p></span></p>
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<p class="MsoNormal" style="line-height:15.0pt"><span style="font-family:"Trebuchet MS","sans-serif"">Jean-Daniel Fekete, INRIA<o:p></o:p></span></p>
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<p class="MsoNormal" style="line-height:15.0pt"><span style="font-family:"Trebuchet MS","sans-serif"">Jaakko Peltonen, Aalto University<o:p></o:p></span></p>
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<p class="MsoNormal" style="line-height:15.0pt"><span style="font-family:"Trebuchet MS","sans-serif"">Ata Kaban, University of Birmingham<o:p></o:p></span></p>
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<p class="MsoNormal" style="line-height:15.0pt"><span style="font-family:"Trebuchet MS","sans-serif"">John Lee, Universite catholique de Louvain<o:p></o:p></span></p>
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<p class="MsoNormal" style="line-height:15.0pt"><span style="font-family:"Trebuchet MS","sans-serif"">Samuel Kaski, Aalto University<o:p></o:p></span></p>
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<p class="MsoNormal" style="line-height:15.0pt"><span style="font-family:"Trebuchet MS","sans-serif"">Frank-Michael Schleif, University of Bielefeld<o:p></o:p></span></p>
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<p class="MsoNormal" style="line-height:15.0pt"><span style="font-family:"Trebuchet MS","sans-serif"">Leishi Zhang, University of Konstanz<o:p></o:p></span></p>
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<p class="MsoNormal" style="line-height:15.0pt"><span style="font-family:"Trebuchet MS","sans-serif"">Michel Verleysen, Universite catholique de Louvain<o:p></o:p></span></p>
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<p class="MsoNormal" style="line-height:15.0pt"><span style="font-family:"Trebuchet MS","sans-serif"">Nicolas Heulot, CEA LIST<o:p></o:p></span></p>
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