representation related problems in pattern...
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5/10/06 R.P.W. Duin 1
Representation Related Problems in
Pattern Recognition
London, 6 October 2006
Robert P.W. Duin
Delft University of Technology
The Netherlands
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5/10/06 R.P.W. Duin 2
Model Driven ←→ Data Driven
Model of a house Are these houses?
Observations of houses x1
x2
Non-Houses
Houses
Feature Space Representation
Classifier
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5/10/06 R.P.W. Duin 3
The Pattern Recognition System
(area)
(perimeter)x1
x2
Class A
Class B
Objects
Decision Function Generalization
Sensor Representation Generalization
Feature Space Representation
Learning from examplesFinding concepts (classes) from observations
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5/10/06 R.P.W. Duin 4
Representation
Object representationVector representations
FeaturesSamples (Pixels)DissimilaritiesDimensionality problems
Class representationSampling: aselective - selectiveSupervised - UnsupervisedNumber of objects
Non-vectorial representations
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5/10/06 R.P.W. Duin 5
Feature Representation
(area)
(perimeter)x1
x2
Class A
Class BObjects
Feature Space
Due to reduction essentially different objects are represented identically
The feature representation needs a statistical (probabilistic) generalization
x
ProbabilityDensity
PAFA(x)PBFB(x)
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5/10/06 R.P.W. Duin 6
Feature Space Assumptions, The Ideal World• A (small) set of informative features
• Euclidean analysis is possible (after feasible corrections)
• Classes have known, not very different priors
• Natural classes (e.g. correspond to a unsupervised clustering result)
• Training set is representative for the probleme.g. aselectively drawn from the same universe as the test setsufficiently large for the given feature sizeclasses do not driftlabels are correct
In this ideal world we can nicely study generalization procedures Applicable?
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5/10/06 R.P.W. Duin 7
Classifier typology
Can we create a library of problems corresponding to the library of classifiers?
Fisher Bayes Normal
Decision Tree Neural Network
Each classifier has a problem for which it is the best classifier
Nearest Mean
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Classifier Problem Archtypes
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5/10/06 R.P.W. Duin 9
Bad Features → More Features → Complexity Problem
Number of features (parameters) K
ε sample size
The real world, first problem: peaking
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5/10/06 R.P.W. Duin 10
No Feature Reduction
The feature representation enforces class overlap. To be solved by a probabilistic approach.
However:
Are densities needed in high dimensional spaces?Are classes to be represented by densities?
Can we construct domain based classifiers?
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5/10/06 R.P.W. Duin 11
Example Dissimilarity Space: NIST Digits 3 and 8
Examples of the raw data
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5/10/06 R.P.W. Duin 12
Peaking
100 101 1020
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Feature size
Mea
n cl
assi
ficat
ion
erro
r (10
exp
.)
Feature curves for 16 x 16 NIST 3-8
304060100
Sample size
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5/10/06 R.P.W. Duin 13
Overpeaking
100 101 1020
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Feature size
Mea
n cl
assi
ficat
ion
erro
r (10
exp
.)
Feature curves for 16 x 16 NIST 3-8
304060100
Sample size
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5/10/06 R.P.W. Duin 14
101
102
0
0.1
0.2
0.3
0.4
0.5
Small Sample Size
−20 −10 0 10 20−15
−10
−5
0
5
10
15
20
x1
x2
Classification problem R30:
Training Set Size
Averaged error over 50 experiments
Two normal distributions, overlap: ε* = 0.064:feature 1 NA(0, 1), NB(3, 1)feature 2 NA(0, 40), NB(3, 40)feature 3-30NA(0, 1), NB(0, 1)
Bayes Error
Nearest Mean
Fisher LD
Feature Size
True Error
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5/10/06 R.P.W. Duin 15
Dimension Resonance and Dipping
101
102
0
0.1
0.2
0.3
0.4
0.5
Nearest Mean
Pseudo Fisher LD
True Error
Training Set Size
Averaged error over 50 experiments
Fisher LD
feature size
’dipping’(Marco Loog)
’dimension resonance’
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5/10/06 R.P.W. Duin 16
Support Vector Machine for Small Sample Sizes
101
102
0
0.1
0.2
0.3
0.4
0.5
Nearest Mean
Pseudo Fisher LD
True
Training Set Size
SVM
Error
Averaged error over 50 experiments
Fisher LD
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5/10/06 R.P.W. Duin 17
Pixel Representation: Samples Instead of Features
FeaturesShapeMomentsFourier descriptorsFacesMorphology
Class A
Class B Feature Space
Pixels are more general, initially complete representation Large datasets available → good results for OCR
16 x 16 R256Pixels
x1
x2
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5/10/06 R.P.W. Duin 18
The Connectivity Problem in the Pixel Representation
Spatial connectivity is lost
x1 x2 x3
x1
x2
x3
Dependent (connected) measurements are represented independently,The dependency has to be rediscovered from the data.
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5/10/06 R.P.W. Duin 19
The Connectivity Problem in the Pixel Representation
ReshufflePixels
Feature space
Reshuffling pixels will not change the classification
Training set
Test object
Spatial connectivity is lost
Can connectivity be taken into account in the representation?
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5/10/06 R.P.W. Duin 20
High dimensional data often does not overlap
Complete feature representations, which enable the reconstruction
There is no picture that could be member of different classes.
of human recognizable, may yield separable classes.
In some representations classes are separable
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5/10/06 R.P.W. Duin 21
Domains instead of Densities
No well sampled training sets are needed.
Classifiers still to be developed.
Class structure ←→ Object invariants
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5/10/06 R.P.W. Duin 22
Domain based classification
How to construct domain based classifiers?
Don’t trust class densitiesEstimate for each class a domainAssign new objects to nearest domain
Outlier dependentDistances instead of densities
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5/10/06 R.P.W. Duin 23
No Features: Dissimilarities
A B
X
Given labeled training set T
Unlabeled object x to be classified
The traditional Nearest Neighbor rule (template matching) just finds: label(argmintrainset(di)), without using DT. Can we do any better?
dx = (d1 d2 d3 d4 d5 d6 d7)
DT
d11d12d13d14d15d16d17
d21d22d23d24d25d26d27
d31d32d33d34d35d36d37
d41d42d43d44d45d46d47
d51d52d53d54d55d56d57
d61d62d63d64d65d66d67
d71d72d73d74d75d76d77
=
Define dissimilarity measure dij between raw data of objects i and j
not used by NN rule
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5/10/06 R.P.W. Duin 24
No Features: Dissimilarities
A B
X
Given labeled training set T
Unlabeled object x to be classified
The traditional Nearest Neighbor rule (template matching) just finds: label(argmintrainset(di)), without using DT. Can we do any better?
dx = (d1 d2 d3 d4 d5 d6 d7)
DT
d11d12d13d14d15d16d17
d21d22d23d24d25d26d27
d31d32d33d34d35d36d37
d41d42d43d44d45d46d47
d51d52d53d54d55d56d57
d61d62d63d64d65d66d67
d71d72d73d74d75d76d77
=
Define dissimilarity measure dij between raw data of objects i and j
not used by NN rule
Pekalska, The DissimilarityRepresentation for PR,World Scientific, 2005
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5/10/06 R.P.W. Duin 25
Example: Deformable Templates
A.K. Jain, D. Zongker, Representation and recognition of handwritten digit using deformable templates,
IEEE-PAMI, vol. 19, no. 12, 1997, 1386-1391.
Matching new objects x to various templates y
class x( ) class minarg y D x y,( )( )( )=
Examples of deformed templates
Dissimilarity measure appears to be non-metric
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Three Approaches Compared for the Zongker Data
0 500 1000 15000
0.1
0.2
Size of the representation set R
Ave
rage
d ge
nera
lizat
ion
erro
r
Digit data
RLDC; Rep. SetLP; Rep. SetRLDC; Embed.1−NN3−NN
Nearest neighbor Rule
Dissimilarity Space
Embedding
Dissimilarity Space better than Embedding better than Nearest Neighbor Rule
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The Non-Euclidean World of Pattern Recognition
14.9
7.8 4.1
object 78
object 419
object 425
D(A,C)A
B
C
D(A,C) > D(A,B) + D(B,C)
D(A,B) D(B,C)
J A B,( )µA µB– 2
σA2 σB
2+-------------------------=
A BC
J(A,C) = 0; J(A,B) = large; J(C,B) = small ≠ J(A,B)
Weighted edit-distance for strings Single Linkage Clustering
µA µB–
x
σA σB
The Fisher Criterion
Bunke’s Chicken Dataset
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Class Representation Problems
Recognition
system
training set
representativesubset
universe
objects to be recognized
training
execution
What to do if no good definitionof the universe can be found?
unknown priorsskewed problemsill sampled problemslabel uncertaintypopulation drift
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ROC, AUC
εclass_1
1− εclass_2
Receiver Operator Curve (ROC)
Area Under the Curve (AUC)
AUC: Robust performance measure(unknown priors/costs, unbalanced sampling)
Feature Space
Healthy
Disease
Borderline
AUC optimizing classifiers may find ’good’ directionsin case of higly overlapping, ill defined classes1 2
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One-class problems
What is a proper one-class classifier?
training set of a single class only + an outlier in a sea of outliers
How to generalize well: no empty areas includedstay outside boundary objects
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An Opportunity: Large Unlabeled Training Set
• Given: A large, but finite, unlabeled training set Xu, or a density function.
• Ask labels for a small set of objects (of given size), Xl.
• Task: design a classifier, or label Xu.
-10 -5 0 5-10
-5
0
5
Feature 1
Feat
ure
2
Unlabeled Training Set
-10 -5 0 5-10
-5
0
5
Feature 1
Feat
ure
2
Dataset Density
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Approaches
Selective Sampling: • Determine a small set of objects from Xu that represents the dataset well
• Ask for the labels: Xl
• Train a classifier
Active learning• Select (at random?) an initially small training set. Ask for the labels, Xl
• Compute a classifier
• Select, given the classifier and Xu, more objects, ask the labels, extend Xl
• Repeat
Semi-Supervised Learning• Compute classifiers from Xl combined with Xu
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The use of unlabeled objects and active learning
How to make use of unlabeled data to construct classifiers?
Can we make us of unlabeled objectsfor better classification?
Can we select a few to improvethe classifier?- close to the decision boundary?- far away from the dec. boundary?- at random?
Assume labeling is expensive
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Active Learning: Strategies
ExploitationAdd unlabeled objects close the classifier to the training set.
ExplorationAdd remote unlabeled objects that represent unvisited clusters.
Is the set of objects representative for the problem?
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Semi - Supervised Learning
-10 -5 0 5-10
-5
0
5
Feature 1
Feat
ure
2
Partially labeled dataset Can better classifiers be designed by using labeled and unlabeled objects simultaneously?
Two possible approaches:- Combine supervised and unsupervised models- Label propagation
Application: learn from the test set!
How to build a good semi-supervised classifier?
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Semi Supervised Learning: Combining Supervised and Unsupervised Models
I. Cohen, F.G. Cozman, N. Sebe,M.C. Cirelo,T.S. Huang, Semisupervised learning of classifiers: theory, algorithms,
and their application to human-computer interaction, IEEE-PAMI, 26, 1553-1566, 2004.
Another example of ’dipping’?
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Learning from the Test Set
2 x 2 training samples and x 98 test samples
20 iterations of soft Parzen
Piotr Juszczak, Learning to recognise, Ph.D. Thesis, Delft Univ. of Technology, 2006see also Cores 2005.
Soft label propagation
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Semi-Supervised Learning by Soft Parzen
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-1 0 1 2 3
-4
-2
0
2
4
6
Feature 1
Feat
ure
2
The One-Object Classifier (OOC)
50 100 150 2000
0.05
0.1
0.15
0.2
0.25
0.3
Ave
rage
d er
ror (
25 e
xper
imen
ts)
Learning curve on Highleyman Dataset
Bayes-Normal-2One Object Classifier
1. Cluster the dataset into two clusters.
2. Select a most ’typical’ object in one of the clusters.
3. Ask for its label.
4. Label the clusters accordingly.
5. Compute the classifier.
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Highleyman Dataset
oocqdc
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Conclusions
Pattern recognition research is solving representation problems