Duality in a maximum generalized entropy model

Sep 26, 2014 - -regularization (Phillip, Anderson, Schapire 2006). GIS (Geographic Information System). GBIF (Global Biodiversity Inventory Facility) in R ...
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MaxEnt, 2014 21-26 September 2014 Chateau Clos Luce, Amboise

Duality in a maximum generalized entropy model Shinto Eguchi Osamu Komori Atsumi Ohara

MaxEnt in ecology

Presence data

obtained by an ecological study

Equal mean space

Log-entropy (Boltzmann-Gibbs-Shannon)

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Maximum entropy distribution Problem : find a distribution f to maximize H( f under the constraints:

Solution is Gibbs distribution

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MAP estimator Loss function

Sequential L1-regularization (Phillip, Anderson, Schapire 2006) Cf. adaptive Lasso, Zou (2006, JASA) GIS (Geographic Information System) GBIF (Global Biodiversity Inventory Facility) in R

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Habitat map of right whales

http://www.seascapemodeling.org/

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Area Under ROC Curve

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Problems in MaxEnt MaxEnt is useful in presence-only-data in ecological studies MaxEnt jointly suggests the statistical model and estimation, (exponential model and maximum likelihood) Model selection and model validation have simple forms

The duality between the model and estimation is not robust (model misspecification, over-fitting to data, cf. Bayes robust) U-entropy leads to extension from MaxEnt to MaxU-Ent (The duality is extended in functional degree of freedom)

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Hill’s diversity numbers (effective number of species) (1973)

richness exp (log-entropy) Simpson index

Rare species are highly weighted All species are fairly weighted Dominant species are highly weighted 8

Several measures of entropy Simpson (1949)

Hill’s diversity (1973)

Tsallis entropy (1988)

U-entropy (Eguchi, 2006)

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Generalized entropy Generator Convex conjugate

U-entropy

Example

log entropy

Renyi-Tsallis entropy

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Generalized divergence U-cross entropy U-divergence

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MaxU-Ent Problem : find a distribution f to maximize HU ( f under the constraints:

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Minimum divergence geometry

Riemannian metric Linear connections

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Dually flat U-model

Riemann metric

Linear connections Conjugate convexity:

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Pythagorean theorem for U-divergence Theorem

m-geodesic U-geodesic

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MaxEnt and MinDiv

Max-Entropy model

Duality of totally geodesic

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MaxU-Ent = MaxU-model + MinU-divergence

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Project Hill’s diversity numbers associate with one parameter family of MaxEnt methods, { Max -Ent :  in R }

Can the family improve the classical Maxent? Which -MaxEnt is the best ? Can we select the best  based on presence data?

Test AUC comparison may suggest better Max -Ent. 19

Folivora ( Three-toed sloth )

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Max-Ent

MaxEnt)

 

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Max-Ent

MaxEnt)



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A class of MaxU-Ent

MaxEnt Log-entropy Gibbs distribution KL-divergence MLE

U-MaxEnt U-MaxEnt U-entropy U-MaxEnt U-entropy U-Gibbs distribution MaxU-Ent U-entropy U-Gibbs distribution U-estimator U-entropy U-Gibbs distribution U-estimator U-divergence U-Gibbs distribution U-estimator U-divergence U-divergence U-divergence MinU-estimator 23

Thank you!

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Model validation

A brief tutorial on Maxent (Phillips, 2006)

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( U-estimator , U-model) model exp-model

U-model

loss function

- log-likelihood

U-loss

Robust model MLE

Robust estimator U-estimate

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MaxU-Ent Problem : find a distribution f to maximize HU ( f under the constraints:

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Related problems Maxent is equivalent to Poisson point process model ( Renner-Warton, 2013, Loyle et all. 2012, Aarts et al. 2012, Fithian-Hastie, 2012)

-Maxent is equivalent to -Poisson point process model?

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-Poisson distribution 0.35

0.35

0.30

0.30

0.25 0.25

0.20 0.20

0.15 0.15

0.10 0.10

0.05 0.05

5

10

15

20 5

10

15

20

0.35 0.35

0.30

0.30

0.25 0.25

0.20 0.20

0.15 0.15

0.10

0.10

0.05

0.05

5

10

15

20

5

10

15

20

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Habitat map

 = 0

 = 0.5 30