Transcript part 4 - Electrical and Computer Engineering

Part 4:
ADVANCED SVM-based
LEARNING METHODS
Vladimir Cherkassky
University of Minnesota
[email protected]
Presented at Tech Tune Ups, ECE Dept, June 1, 2011
Electrical and Computer Engineering
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OUTLINE
•
•
Motivation for non-standard
approaches: high-dimensional data
Alternative Learning Settings
- Transduction and SSL
- Inference Through Contradictions
- Learning using privileged information (or SVM+)
- Multi-task Learning
•
Summary
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Insights provided by SVM(VC-theory)
• Why linear classifiers can generalize?
(1) Margin is large (relative to R)
(2) % of SV’s is small
(3) ratio d/n is small
• SVM offers an effective way to control
complexity (via margin + kernel selection)
i.e. implementing (1) or (2) or both
• What happens when d>>n ?
- standard inductive methods usually fail
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How to improve generalization for HDLSS?
Conventional approach:
Incorporate a priori knowledge into learning method
• Preprocessing and feature selection
• Model parameterization (~ good kernels in SVM)
Assumption: a priori knowledge about good model
Non-standard learning formulations:
Incorporate a priori knowledge into new non-standard
learning formulation (learning setting)
Assumption: a priori knowledge is about properties
of application data and/or goal of learning
• Which type of assumptions makes more sense?
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OUTLINE
• Motivation for non-standard
approaches
• Alternative Learning Settings
- Transduction and SSL
- Inference Through Contradictions
- Learning with Structured Data
- Multi-task Learning
• Summary
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Examples of non-standard settings
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•
•
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Application domain: hand-written digit recognition
Standard inductive setting
Transduction: labeled training + unlabeled data
Learning through contradictions:
labeled training data ~ examples of digits 5 and 8
unlabeled examples (Universum) ~ all other (eight) digits
• Learning using hidden information:
Training data ~ t groups (i.e., from t different persons)
Test data ~ group label not known
• Multi-task learning:
Training data ~ t groups (from different persons)
Test data ~ t groups (group label is known)
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Modifications of Inductive Setting
• Standard Inductive learning assumes
Finite training set x i , yi 
Predictive model derived using only training data
Prediction for all possible test inputs
• Possible modifications
1. Predict only for given test points  transduction
2. A priori knowledge in the form of additional ‘typical’
samples  learning through contradiction
3. Additional (group) info about training data  Learning
using privileged information (LUPI) aka SVM+
4. Additional (group) info about training + test data 
Multi-task learning
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Transduction (Vapnik, 1982, 1995)
• How to incorporate unlabeled test data into the
learning process? Assume binary classification
• Estimating function at given points
Given: labeled training data
x i , yi  i  1,...,n
and unlabeled test points
j  1,...,m
x*j
 
Estimate: class labels y *  ( y1* ,....ym* ) at these test points
Goal of learning: minimization of risk on the test set:
1 m
R( y )  
m j 1
*
*
*
*
*
*

y

f
(
x
,

),....
f
(
x
where
L
y
,
y
dP
(
y
/
x
)
1
m , ) 
j
j



y
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Induction vs Transduction
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Transduction based on margin size
Single unlabeled test point X
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Many test points X aka working samples
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Transduction based on margin size
• Binary classification, linear parameterization,
joint set of (training + working) samples
• Two objectives of transductive learning:
(TL1) separate labeled training data using a largemargin hyperplane (as in standard inductive SVM)
(TL2) separating (explain) working data set using a
large-margin hyperplane.
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Transduction based on margin size
• Standard SVM hinge loss for labeled samples
• Loss function for unlabeled samples:
 Mathematical optimization formulation
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Optimization formulation for SVM transduction
• Given: joint set of (training + working) samples
• Denote slack variables  i for training, *j for working
1
R
(
w
,
b
)

(w  w)  C    C  
• Minimize
2
subject to  y*i [(w  x i )  b]  1   i*
n
m
*
i 1
i
j 1
*
j
y j [(w  x i )  b]  1   j

 ,  *  0, i  1,...,n, j  1,...,m
i
j

y*j  sign(w  x j  b), j  1,...,m
where
 Solution (~ decision boundary) D(x)  (w*  x)  b*
• Unbalanced situation (small training/ large test)
 all unlabeled samples assigned to one class
1 n
1 m
• Additional constraint: n  yi  m [(w  x i )  b]
i 1
j 1
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Optimization formulation (cont’d)
• Hyperparameters C and C * control the trade-off
between explanation and margin size
• Soft-margin inductive SVM is a special case of
soft-margin transduction with zero slacks  *j  0
• Dual + kernel version of SVM transduction
• Transductive SVM optimization is not convex
(~ non-convexity of the loss for unlabeled data) –
 different opt. heuristics ~ different solutions
• Exact solution (via exhaustive search) possible for
small number of test samples (m) – but this
solution is NOT very useful (~ inductive SVM).
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Many applications for transduction
• Text categorization: classify word documents
into a number of predetermined categories
• Email classification: Spam vs non-spam
• Web page classification
• Image database classification
• All these applications:
- high-dimensional data
- small labeled training set (human-labeled)
- large unlabeled test set
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Example application
• Prediction of molecular bioactivity for drug
discovery
• Training data~1,909; test~634 samples
• Input space ~ 139,351-dimensional
• Prediction accuracy:
SVM induction ~74.5%; transduction ~ 82.3%
Ref: J. Weston et al, KDD cup 2001 data analysis: prediction
of molecular bioactivity for drug design – binding to
thrombin, Bioinformatics 2003
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Semi-Supervised Learning (SSL)
• Labeled data + unlabeled data  Model
• Similar to transduction (but not the same):
- Goal 1 ~ prediction for unlabeled samples
- Goal 2 ~ estimate an inductive model
• Many algorithms
• Applications similar to transduction
• Typically
- Transduction works better for HDLSS
- SSL works better for low-dimensional data
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Example: Self-Learning Algorithm
Given initial labeled set L and unlabeled set U
Repeat:
(1) estimate a classifier using labeled set L
(2) classify randomly chosen unlabeled sample
using decision rule estimated in Step (1)
(3) move this new labeled sample to set L
Iterate steps (1) – (3) until all unlabeled
samples are classified.
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Example of Self-Learning Algorithm
Noisy Hyperbolas: unlabeled samples in green
Initial condition:
Iteration 1
0.9
0.8
Unlabeled Samples
Class +1
Class -1
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.1
0.2
0.3
0.4
0.5
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0.8
0.9
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Example of Self-Learning Algorithm
Iteration 50
Iteration 100 (final)
Iteration 50
0.9
0.8
Iteration 100
0.9
Unlabeled Samples
Class +1
Class -1
0.8
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0.5
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Class +1
Class -1
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0.9
0.1
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0.9
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Inference through contradiction (Vapnik 2006)
• Motivation: what is a priori knowledge?
- info about the space of admissible models
- info about admissible data samples
• Labeled training samples + unlabeled samples
from the Universum
• Universum samples encode info about the region
of input space (where application data lives):
- Usually from a different distribution than
training/test data
• Examples of the Universum data
• Large improvement for small training samples
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Inference through contradictions
aka Universum learning
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Main Idea
•
Handwritten digit recognition: digit 5 vs 8
Fig. courtesy of J. Weston (NEC Labs)
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Learning with the Universum
• Inductive setting for binary classification
x i , yi  i  1,...,n
Given: labeled training data
and unlabeled Universum samples x*j
j  1,...,m
Goal of learning: minimization of prediction risk (as in
standard inductive setting)
 
• Balance between two goals:
- explain labeled training data using large-margin
hyperplane
- achieve maximum falsifiability ~ max # contradictions
on the Universum
 Math optimization formulation (extension of SVM)
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 -insensitive loss for Universum samples
y

1

2*
x
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Random averaging Universum
Class 1
Average
Class -1
Hyper-plane
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Random Averaging for digits 5 and 8
• Two randomly selected examples
• Universum sample:
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Application Study (Vapnik, 2006)
• Binary classification of handwritten digits 5 and 8
• For this binary classification problem, the following
Universum sets had been used:
U1: randomly selected digits (0,1,2,3,4,6,7,9)
U2: randomly mixing pixels from images 5 and 8
U3: average of randomly selected examples of 5 and 8
Training set size tried: 250, 500, … 3,000 samples
Universum set size: 5,000 samples
• Prediction error: improved over standard SVM, i.e.
for 500 training samples: 1.4% vs 2% (SVM)
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Cultural Interpretation of Universum:
jokes, absurd examples:
neither Hillary nor Obama
dadaism
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Application Study:
predicting gender of human faces
Binary classification setting
Difficult problem:
dimensionality ~ large (10K - 20K)
labeled sample size ~ small (~ 10 - 20)
Humans perform very well for this task
Issues:
- possible improvement (vs standard SVM)
- how to choose ‘good’ Universum?
- model parameter tuning
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Male Faces: examples
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Female Faces: examples
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Universum Faces:
neither male nor female
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Empirical Study (cont’d)
• Universum generation:
U1 Average: of male and female samples randomly
selected from the training set (U. of Essex database)
U2 Empirical Distribution: estimate pixel-wise
distribution of the training data. Generate a new
picture from this distribution
U3 Animal faces:
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Universum generation: examples
• U1 Averaging:
• U2 Empirical Distribution:
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Results of gender classification
• Classification accuracy: improves vs standard
SVM by ~ 2% with U1 Universum,
and by ~ 1% with U2 Universum.
• Universum by averaging gives better results for
this problem, when number of Universum samples
N = 500 or 1,000
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Results of gender classification
• Universum ~ Animal Faces:
• Degrades classification accuracy by 2-5%
(vs standard SVM)
• Animal faces are not relevant to this problem
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Learning with Structured Data(Vapnik, 2006)
• Application: Handwritten digit recognition
Labeled training data provided by t persons (t >1)
Goal 1: find a classifier that will generalize well for future
samples generated by these persons ~ Learning with
Structured Data or Learning using Hidden Information
Goal 2: find t classifiers with generalization (for each person)
~ Multi-Task Learning (MTL)
• Application: Medical diagnosis
Labeled training data provided by t groups of patients (t >1),
say men and women (t = 2)
Goal 1: estimate a classifier to predict/diagnose a disease
using training data from t groups of patients ~ LWSD
Goal 2: find t classifiers specialized for each group of patients
~ MTL
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Different Ways of Using Group Information
sSVM:
SVM
SVM+
SVM+:
mSVM:
MTL:
f(x)
f(x)
SVM
f1(x)
SVM
f2(x)
f1(x)
svm+MTL
f2(x)
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SVM+ technology (Vapnik, 2006)
• Map the input vectors simultaneously into:
- Decision space (standard SVM classifier)
- Correcting space (where correcting functions
model slack variables for different groups)
• Decision space/function ~ the same for all groups
• Correcting functions ~ different for each group
(but correcting space may be the same)
• SVM+ optimization formulation incorporates:
- the capacity of decision function w, w 
- capacity of correcting functions w r , w r  for group r
- relative importance (weight)  of these two capacities
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SVM+ approach (Vapnik, 2006)
Correcting space
1
Correcting functions
2
mapping
Correcting space
1
mapping
Decision function
2
Decision space

r
slack variable for group r
Group1
Group2
Class 1
Class -1
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SVM+ Formulation
xi
z i   z (xi )  Z
Decision Space
z ri   zr (xi )  Zr
Correcting Space
y  sign[ f (x)]  sign[(w,  Z (x))  b]
min
w, w1 ,..,wt ,b , d1 ,..d t
t
1
 t
(w, w)   (w r , w r )  C 
2
2 r 1
r 1

iTr
r
i
subject to:
yi ((w, z i )  b)  1   ir , i  Tr , r  1,...,t
 ir  0, i  Tr , r  1,...,t
ir  (z ir , w r )  d r , i  Tr , r  1,...,t
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SVM+ for Multi-task Learning (Liang 2008)
• New learning formulation: SVM+MTL
• Define decision function for each group as
f r (x)  ( z (x), w)  b  ( zr (x), w r )  d r , r  1,...,t
• Common decision function ( z (x), w)  b
models the relatedness among groups
• Correcting functions fine-tune the model for
each group (task)
.
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svm+MTL Formulation
xi
z i   z (xi )  Z
Decision Space
z ri   zr (xi )  Zr
Correcting Space
f r (x)  sign(( z (x), w)  b  ( zr (x), wr )  dr ) , r  1,...,t
min
w, w1 ,..,wt ,b ,d1 ,..d r
t
1
 t
(w, w)   (w r , w r )  C  ir
2
2 r 1
r 1 iTr
subject to:
yir ((w, z i )  b  (w r , z ir )  d r )  1   ir , i Tr , r  1,...,t
ir  0, i T , r  1,...,t
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Empirical Validation
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Different ways of using group info 
different learning settings:
- which one yields better generalization?
- how performance is affected by sample
size?
Empirical comparisons:
- synthetic data set
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Different Ways of Using Group Information
sSVM:
SVM
SVM+
SVM+:
mSVM:
MTL:
f(x)
f(x)
SVM
f1(x)
SVM
f2(x)
f1(x)
svm+MTL
f2(x)
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Comparison for Synthetic Data Set
• Generate x  R 20 where each xi ~ uniform(1,1)•, i  1,...,20
• The coefficient vectors of three tasks are specified as
β1  [1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0]
β 2  [1,1,1,1,1,1,1,1,0,1,0,1,0,0,0,0,0,0,0,0]
β3  [1,1,1,1,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0]
• For each task and each data vector, y  sign(i x  0.5)
• Details of methods used:
- linear SVM classifier (single parameter C)
- SVM+, SVM+MTL classifier (3 parameters: linear
kernel for decision space, RBF kernel for correcting
space, and parameter γ)
- Independent validation set for model selection
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Experimental Results
• Comparison results (ave over 10 trials):
n ~ number of training samples per task
ave test error (%):
Methods:
n=15
n=100
sSVM SVM+ mSVM SVM+MTL
19.9
11.9
19.1
11.7
29.3
8.8
20.8
8.5
Note: relative performance depends on sample size
Note:
SVM+ always better than SVM
SVM+MTL always better than mSVM
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OUTLINE
•
•
•
Motivation for non-standard approaches
Alternative Learning Settings
Summary: Advantages/limitations of nonstandard settings
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Advantages+limitations of nonstandard settings
• Advantages
- make common sense
- follow methodological framework (VC-theory)
- yield better generalization (but not always)
•
Limitations
- need to formalize application requirements
 need to understand application domain
- generally more complex learning formulations
- more difficult model selection
- few known empirical comparisons (to date)
•
SVM+ is a promising new technology for
hard problems
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References and Resources
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Vapnik, V. Estimation of Dependencies Based on Empirical Data. Empirical
Inference Science: Afterword of 2006, Springer, 2006
Cherkassky, V. and F. Mulier, Learning from Data, second edition, Wiley, 2007
Chapelle, O., Schölkopf, B., and A. Zien, Eds., Semi-Supervised Learning, MIT
Press, 2006
Cherkassky, V. and Y. Ma, Introduction to Predictive learning, Springer, 2011
(to appear)
Hastie, T., R. Tibshirani and J. Friedman, The Elements of Statistical Learning.
Data Mining, Inference and Prediction, New York: Springer, 2001
Schölkopf, B. and A. Smola, Learning with Kernels. MIT Press, 2002.
Public-domain SVM software
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Main web page link http://www.kernel-machines.org
LIBSVM software library http://www.csie.ntu.edu.tw/~cjlin/libsvm/
SVM-Light software library http://svmlight.joachims.org/
Non-standard SVM-based methodologies: Universum, SVM+, MTL
http://www.ece.umn.edu/users/cherkass/predictive_learning/
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