Seminario prof. Leo Liberti - 14 novembre ore 15.30 aula Anile

Martedì 14 novembre dalle 15.30 alle 17.00 in Aula Anile il Professore Leo Liberti del CNRS LIX Ecole polytechnique (France) terrà un seminario dal titolo: Distance Geometry in Data Science.

Tutti gli interessati sono invitati a partecipare (con preghiera di diffusione).

Abstract

Many problems in data science are addressed by mapping entities of various kind (e.g. words, finite sets, relations, graphs, orders, lists, files, analogue or digital signal streams, rows or columns in a database table) to vectors in a Euclidean space of some dimension, which is home to several well-known and efficient methods. Most of these methods (e.g. Multidimensional Scaling, Principal Component Analysis, K-means clustering, random projections) are based on the proximity of pairs of vectors. In order for the results of these methods to make sense when mapped back, the proximity of entities in the original problem must be well approximated in the Euclidean space setting. If proximity were known for each pair of original entities, this mapping would be a good example of isometric embedding. Usually, however, this is not the case, as data are partial, wrong and noisy. I shall survey some of the methods above from the point of view of Distance Geometry.


Data di pubblicazione: 13/11/2017

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