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<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""><font class="">The next Mobilize Center Seminar is scheduled for Thursday, May 3rd, and features
<span style="white-space: pre-wrap;" class=""><b style="font-style: italic;" class="">Abubakar Abid</b>, Biomedical Data Science Department, Stanford University.</span></font><span class=""> He will be presenting
</span><font class="">“<b class="">Contrastive PCA</b><b class="">: A Method for Exploring Patterns Enriched in a Dataset</b><span class="">”.</span></font></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font class="" face="Arial"> </font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font class=""><span class=""><font class="" face="Arial">The Mobilize Center seminars are held once a month. Please check <a href="http://mobilize.stanford.edu/events/" class="">Mobilize Events</a> for updates.</font></span></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font class="" face="Arial"><br class="">
</font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font class="" face="Arial">We look forward to seeing you next wee!<o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font class="" face="Arial"> <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font class="" face="Arial"><b class="">TITLE:</b> <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><b style="font-family: Arial;" class="">Contrastive PCA</b><b class="" style="font-family: Arial;">: A Method for Exploring Patterns Enriched in a Dataset</b></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font class="" face="Arial"><b class=""> </b></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font class="" face="Arial"><b class="">WHEN:</b> <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font class="" face="Arial"><b class="">Thursday, May 3rd</b><o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font class="" face="Arial"><b class="">noon - 1 pm</b> <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font class="" face="Arial"> <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font class="" face="Arial"><b class="">WHERE:</b> <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font class="" face="Arial"><b class=""><span style="color: rgb(251, 2, 7);" class="">Y2E2 300, Stanford University </span></b><o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><b class=""><span style="font-size: 9pt; font-family: Arial, sans-serif;" class=""><br class="">
</span></b></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><b class=""><span style="font-size: 9pt; font-family: Arial, sans-serif;" class="">Abstract:</span></b></div>
<div style="margin: 0in 0in 0.0001pt;" class="">
<div style="margin: 0in 0in 0.0001pt;" class=""><span class=""><span class=""><span style="color: rgb(33, 33, 33);" class=""><font face="Arial" class="">Visualization and exploration of high-dimensional data is a ubiquitous challenge across disciplines. Widely
used techniques such as principal component analysis (PCA) aim to identify dominant trends in one dataset. However, in many settings we have multiple datasets--- e.g., a treatment and a control experiment--- and we are interested in visualizing and exploring
patterns that are specific to one of the datasets. This paper proposes a new method, contrastive principal component analysis (cPCA), which identifies low-dimensional structures that are enriched in a dataset relative to comparison data. In a wide variety
of experiments, we demonstrate that cPCA with a background dataset enables us to visualize dataset-specific patterns that are not captured by PCA and other standard methods. We further provide a geometric interpretation of cPCA and provide strong mathematical
guarantees for the method. An efficient implementation of cPCA is publicly available, and can be used for exploratory data analysis in many applications where PCA is currently used.</font></span></span></span></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><span class=""><span class=""><font face="Arial, sans-serif" style="font-weight: bold; font-size: 9pt;" class=""><br class="">
</font></span></span></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><span class=""><span class=""><font face="Arial, sans-serif" style="font-weight: bold; font-size: 9pt;" class="">Bio:</font></span></span></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""><a name="_MailEndCompose" class=""><font color="#000000" class="">Abubakar Abid is a 2nd year PhD student who develops machine learning methods, particularly those with applications
to medicine and biology. He is advised by Professor James Zou, from the Biomedical Data Science department at Stanford. Abubakar is a 2016 Paul and Daisy Soros Fellow and a recipient of the Stanford Graduate Fellowship.</font></a><a name="_MailEndCompose" class=""><font color="#000000" class="">Abubakar
Abid is a 2nd year PhD student who develops machine learning methods, particularly those with applications to medicine and biology. He is advised by Professor James Zou, from the Biomedical Data Science department at Stanford. Abubakar is a 2016 Paul and Daisy
Soros Fellow and a recipient of the Stanford Graduate Fellowship.</font></a></font></div>
<p class="MsoNormal"><font class=""><font face="Arial" class="">To keep up-to-date on upcoming speakers and the dates, visit </font></font><a href="http://mobilize.stanford.edu/events/" class="" style="font-family: Arial;">Mobilize Events</a><font class="" style="font-family: Arial;">.</font></p>
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<div style="margin: 0in 0in 0.0001pt;" class=""><span class=""><font face="Arial" class="">Diane Bush</font></span></div>
<div class="">
<div class=""><font face="Arial" class="">Assistant to Professor Scott Delp<br class="">
NMBL, Mobilize Center, OpenSim<br class="">
Stanford University<br class="">
<a href="mailto:dbush1@stanford.edu" class="">dbush1@stanford.edu</a><br class="">
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