Seite - 400 - in Differential Geometrical Theory of Statistics
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Entropy2016,9, 337
although the second form is probablymore efficient fromanumerical point of view andwill be
investigated inasecondstage.
Having the density at hand, the gradient of the entropy with respect to the points
γij, i = 1,. . . ,N, j = 1,. . . ,m can be easily computed using a straightforward application of the
formula (19).Whendealingwithplanarcurves,asimplificationoccurs for thesecondderivative term
since forasmoothcurveγj: ( γ′′j (t)
‖γ′j(t)‖ )
N = κ(t)N(t).
where κ is the curvature andN theunit normal vector. These quantitiesmaybe computedusing
numerical differentiation, but a coarse approximation based on the rotation rate of the vectors
γi,j+1−γi,j,γi,j+2−γi,j+1workswell inmanycases.
Thecaseof scaledarclengthparametrizationneedssomeextraattention,due to thecondition
on the tangential component. The simplest approach is to move the points γij according to an
unconstrainedgradient, thentore-sample theobtainedcurvesoas togetadjustedγij thatcorrespond
to theabscissaηj, j=1,. . . ,m.
Inanumerical implementation, thescalingfactor infrontof thewholeexpressionmaybedropped
duetothefact thatallgradient-basedalgorithmswilluseanautomatically-tunedsteplength.Asusual
withgradientalgorithms,onemustcarefullyselect thesteptakinginthemaximizingdirectioninorder
toavoiddivergence.Asimplefixedstepstrategywasfirstappliedandgivessatisfactoryresultson
smalldatasets.Asaferapproach is toadapt thestepsizesoas toensureasufficientdecreaseof the
entropy.Duetothepotentiallyhugedimensionof thesearchspace, thisprocedurehas tobesimple
enough.Anapproximatequadratic search[12]wasusedin thefinal implementation.
Theprocedureappliedtoonedayof trafficoverFranceyields thepictureofFigure3.Asexpected,
a route-like network emerges. In such a case, since the traffic comes froman already organized
situation, the recovered network is indeed a subset of the route network in the french airspace.
Pleasenote that there isa trade-offbetweenthedensityconcentrationandtheminimalcurvatureof
therecoveredtrajectories, asalreadymentioned. Thekernelbandwidthwaschosenempirically in the
examplepresented,with theaidofvisual interaction.
(a) (b)
Figure3.Trafficof24February2013: (a) Initial traffic; (b)Bundledtraffic.
In thesecondexampleofFigure4, theproblemofautomaticconflict solving isaddressed. In the
initial situation,aircraftareconvergingtoasinglepoint,which isunsafe.Air trafficcontrollerswill
proceed in sucha case bydiverting aircraft from their initial flightpath so as to avoid eachother,
butonlyusingverysimplemaneuvers.Anautomatedtoolwillmakefulluseof theavailableairspace,
andtheresultingsetof trajectoriesmayfail tobemanageablebyahuman: in theeventofasystem
failure,nobackupcanbeprovidedbycontrollers. Theentropyminimizationprocedurewasaddedto
anautomatedconflict solver inorder toendupwithflightpathsstill tractablebyhumans. Thefinal
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Differential Geometrical Theory of Statistics
- Titel
- Differential Geometrical Theory of Statistics
- Autoren
- Frédéric Barbaresco
- Frank Nielsen
- Herausgeber
- MDPI
- Ort
- Basel
- Datum
- 2017
- Sprache
- englisch
- Lizenz
- CC BY-NC-ND 4.0
- ISBN
- 978-3-03842-425-3
- Abmessungen
- 17.0 x 24.4 cm
- Seiten
- 476
- Schlagwörter
- Entropy, Coding Theory, Maximum entropy, Information geometry, Computational Information Geometry, Hessian Geometry, Divergence Geometry, Information topology, Cohomology, Shape Space, Statistical physics, Thermodynamics
- Kategorien
- Naturwissenschaften Physik