manifold learning using geodesic entropic graphs alfred o. hero and jose costa dept. eecs, dept...
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Manifold Learning Using Geodesic Entropic Graphs
Alfred O. Hero and Jose Costa Dept. EECS, Dept Biomed. Eng., Dept. Statistics
University of Michigan - Ann Arbor [email protected]
http://www.eecs.umich.edu/~hero
Research supported in part by: ARO-DARPA MURI DAAD19-02-1-0262
1. Manifold Learning and Dimension Reduction2. Entropic Graphs3. Examples
1.Dimension Reduction and Pattern Matching
• 128x128 images of three vehicles over 1 deg increments of 360 deg azimuth at 0 deg elevation
• The 3(360)=1080 images evolve on a lower dimensional imbedded manifold in R^(16384)
Courtesy of Center for Imaging Science, JHU
HMMVT62Truck
Land Vehicle Image Manifold
Entropy:
Manifold (intrinsic) Dimension: d
Embediing (extrinsic) Dimension: D
Qua
ntiti
es O
f In
tere
st
Assumption:
is a conformal mappingA statistical sample
Sampling distribution
2dim manifold
Sampling
Embedding
Sampling on a Domain Manifold
Background on Manifold Learning1. Manifold intrinsic dimension estimation
1. Local KLE, Fukunaga, Olsen (1971)2. Nearest neighbor algorithm, Pettis, Bailey, Jain, Dubes (1971) 3. Fractal measures, Camastra and Vinciarelli (2002)4. Packing numbers, Kegl (2002)
2. Manifold Reconstruction1. Isomap-MDS, Tenenbaum, de Silva, Langford (2000)2. Locally Linear Embeddings (LLE), Roweiss, Saul (2000)3. Laplacian eigenmaps (LE), Belkin, Niyogi (2002)4. Hessian eigenmaps (HE), Grimes, Donoho (2003)
3. Characterization of sampling distributions on manifolds1. Statistics of directional data, Watson (1956), Mardia (1972)2. Data compression on 3D surfaces, Kolarov, Lynch (1997) 3. Statistics of shape, Kendall (1984), Kent, Mardia (2001)
MST and Geodesic MST• For a set of points in D-
dimensional Euclidean space, the Euclidean MST with edge power weighting gamma is defined as
• edge lengths of a spanning tree over
• When pairwise distances are geodesic distances on obtain Geodesic MST
• For dense samplings GMST length = MST length
Special Cases
• Isometric embedding ( distance preserving)
• Conformal embedding ( angle preserving)
Joint Estimation Algorithm
• Convergence theorem suggests log-linear model
• Use bootstrap resampling to estimate mean MST length and apply LS to jointly estimate slope and intercept from sequence
• Extract d and H from slope and intercept
Bootstrap Estimates of GMST Length
785 790 795 800805
806
807
808
809
810
811
812
813
814
815
n
E[L
n]
Segment n=786:799 of MST sequence (=1,m=10) for unif sampled Swiss Roll
Bootstrap SE bar (83% CI)
loglogLinear Fit to GMST Length
6.665 6.67 6.675 6.68 6.6856.692
6.694
6.696
6.698
6.7
6.702
6.704Segment of logMST sequence (=1,m=10) for unif sampled Swiss Roll
log(n)
log
(E[L
n])
y = 0.53*x + 3.2
log(E[Ln])
LS fit
Dimension and Entropy Estimates
• From LS fit find:• Intrinsic dimension estimate
• Alpha-entropy estimate ( )
– Ground truth:
Application to Faces
• Yale face database 2– Photographic folios of many people’s faces – Each face folio contains images at 585
different illumination/pose conditions– Subsampled to 64 by 64 pixels (4096 extrinsic
dimensions)
• Objective: determine intrinsic dimension and entropy of a typical face folio
Conclusions
• Characterizing high dimension sampling distributions – Standard techniques (histogram, density estimation) fail
due to curse of dimensionality– Entropic graphs can be used to construct consistent
estimators of entropy and information divergence – Robustification to outliers via pruning
• Manifold learning and model reduction– LLE, LE, HE estimate d by finding local linear
representation of manifold– Entropic graph estimates d from global resampling – Computational complexity of MST is only n log n
Advantages of Geodesic Entropic Graph Methods
References• A. O. Hero, B. Ma, O. Michel and J. D. Gorman,
“Application of entropic graphs,” IEEE Signal Processing Magazine, Sept 2002.
• H. Neemuchwala, A.O. Hero and P. Carson, “Entropic graphs for image registration,” to appear in European Journal of Signal Processing, 2003.
• J. Costa and A. O. Hero, “Manifold learning with geodesic minimal spanning trees,” accepted in IEEE T-SP (Special Issue on Machine Learning), 2004.
• A. O. Hero, J. Costa and B. Ma, "Convergence rates of minimal graphs with random vertices," submitted to IEEE T-IT, March 2001.
• J. Costa, A. O. Hero and C. Vignat, "On solutions to multivariate maximum alpha-entropy Problems", in Energy Minimization Methods in Computer Vision and Pattern Recognition (EMM-CVPR), Eds. M. Figueiredo, R. Rangagaran, J. Zerubia, Springer-Verlag, 2003