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UID:5546@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20140117T140000
DTEND;TZID=Europe/Paris:20140117T150000
DTSTAMP:20241030T102518Z
URL:https://www.i2m.univ-amu.fr/evenements/f-denis-lif-dimension-free-conc
 entration-bounds-on-hankel-matrices-for-spectral-learning/
SUMMARY: (...): F. Denis (LIF): Dimension-free Concentration Bounds on Hank
 el Matrices for Spectral Learning
DESCRIPTION:: Dimension-free Concentration Bounds on Hankel Matrices for Sp
 ectral Learning\n\nBy François Denis\\\, LIF.\n\nCowork with Mattias Gybe
 ls and Amaury Habrard.\n\nLearning probabilistic models over strings is an
  important issue\nfor many applications. Spectral methods propose elegant 
 solutions to the\nproblem of inferring weighted automata from finite sampl
 es of\nvariable-length strings drawn from an unknown target distribution. 
 These\nmethods rely on a singular value decomposition of a matrix $H_S$\\\
 , called\nthe Hankel matrix\\\, that records the frequencies of (some of) 
 the observed\nstrings. The accuracy of the learned distribution depends bo
 th on the\nquantity of information embedded in $H_S$ and on the distance b
 etween\n$H_S$ and its mean $H_r$. Existing concentration bounds seem to in
 dicate\nthat the concentration over $H_r$ gets looser with the size of $H_
 r$\\\,\nsuggesting to make a trade-off between the quantity of used inform
 ation\nand the size of $H_r$. We propose new dimension-free concentration 
 bounds\nfor several\nvariants of Hankel matrices. Experiments demonstrate 
 that these bounds are\ntight and that they significantly improve existing 
 bounds. These results\nsuggest that the concentration rate of the Hankel m
 atrix around its mean\ndoes not constitute an argument for limiting its si
 ze.
CATEGORIES:Séminaire,Signal et Apprentissage
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