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SUMMARY:Identifying informative distance measures in high-dimensional feat
 ure spaces - Professor Alessandro Laio\, International School for Advanced
  Studies
DTSTART:20240129T140000Z
DTEND:20240129T150000Z
UID:TALK211138@talks.cam.ac.uk
CONTACT:Eszter Varga-Umbrich
DESCRIPTION:Real-world data  typically contain a large number of features 
 that are often heterogeneous in nature\, relevance\, and also units of mea
 sure. When assessing the similarity between data points\, one can build va
 rious distance measures using subsets of these features. Finding a small s
 et of features that still retains sufficient information about the dataset
  is important for the successful application of many statistical learning 
 approaches.\nWe introduce an approach that can assess the relative informa
 tion retained when using two different distance measures\, and determine i
 f they are equivalent\, independent\, or if one is more informative than t
 he other. This test can be used to identify the most informative distance 
 measure  out of a pool of candidates. The approach can be used to identify
  the most appropriate set of collective variables in molecular systems and
  to infer causality in high-dimensional dynamic processes and time series.
LOCATION:Yusuf Hamied Department of Chemistry\, Pfizer Lecture Theatre.
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