### Support Vector Machines and Kernel Methods

Chih-Jen Lin

Department of Computer Science National Taiwan University

Tutorial at ACML, November 8, 2010

### Outline

Basic concepts: SVM and kernels Training SVM

Practical use of SVM

Research directions: large-scale training Research directions: linear SVM

Research directions: others Conclusions

### Outline

Basic concepts: SVM and kernels Training SVM

Practical use of SVM

Research directions: large-scale training Research directions: linear SVM

Research directions: others Conclusions

### Data Classification

Given training data in different classes (labels known)

Predict test data (labels unknown) Training and testing

### Support Vector Classification

Training vectors : x_{i}, i = 1, . . . , l
Feature vectors. For example,
A patient = [height, weight, . . .]^{T}

Consider a simple case with two classes:

Define an indicator vector y
y_{i} =

1 if x_{i} in class 1

−1 if x_{i} in class 2
A hyperplane which separates all data

w^{T}x + b =
h_{+1}

−10

i

A separating hyperplane: w^{T}x + b = 0
(w^{T}xi) + b ≥ 1 if yi = 1
(w^{T}x_{i}) + b ≤ −1 if y_{i} = −1

Decision function f (x) = sgn(w^{T}x + b), x: test data
Many possible choices of w and b

### Maximal Margin

Distance between w^{T}x + b = 1 and −1:

2/kwk = 2/

√
w^{T}w

A quadratic programming problem (Boser et al., 1992)

minw,b

1
2w^{T}w

subject to y_{i}(w^{T}x_{i} + b) ≥ 1,
i = 1, . . . , l .

### Data May Not Be Linearly Separable

An example:

Allow training errors

Higher dimensional ( maybe infinite ) feature space
φ(x) = [φ_{1}(x), φ_{2}(x), . . .]^{T}.

Standard SVM (Boser et al., 1992; Cortes and Vapnik, 1995)

min

w,b,ξ

1

2w^{T}w +C

l

X

i =1

ξ_{i}

subject to y_{i}(w^{T}φ(x_{i})+ b) ≥ 1 −ξ_{i},
ξ_{i} ≥ 0, i = 1, . . . , l .

Example: x ∈ R^{3}, φ(x) ∈ R^{10}
φ(x) = [1,√

2x_{1},√

2x_{2},√

2x_{3}, x_{1}^{2},
x_{2}^{2}, x_{3}^{2},√

2x_{1}x_{2},√

2x_{1}x_{3},√

2x_{2}x_{3}]^{T}

### Finding the Decision Function

w: maybe infinite variables

The dual problem: finite number of variables minα

1

2α^{T}Qα − e^{T}α

subject to 0 ≤ α_{i} ≤ C , i = 1, . . . , l
y^{T}α = 0,

where Q_{ij} = y_{i}y_{j}φ(x_{i})^{T}φ(x_{j}) and e = [1, . . . , 1]^{T}
At optimum

w =Pl

i =1α_{i}y_{i}φ(x_{i})

A finite problem: #variables = #training data

### Kernel Tricks

Q_{ij} = y_{i}y_{j}φ(x_{i})^{T}φ(x_{j}) needs a closed form
Example: x_{i} ∈ R^{3}, φ(x_{i}) ∈ R^{10}

φ(x_{i}) = [1,√

2(x_{i})_{1},√

2(x_{i})_{2},√

2(x_{i})_{3}, (x_{i})^{2}_{1},
(x_{i})^{2}_{2}, (x_{i})^{2}_{3},√

2(x_{i})_{1}(x_{i})_{2},√

2(x_{i})_{1}(x_{i})_{3},√

2(x_{i})_{2}(x_{i})_{3}]^{T}
Then φ(x_{i})^{T}φ(x_{j}) = (1 + x^{T}_{i} x_{j})^{2}.

Kernel: K (x, y) = φ(x)^{T}φ(y); common kernels:

e^{−γkx}^{i}^{−x}^{j}^{k}^{2}, (Radial Basis Function)
(x^{T}_{i} x_{j}/a + b)^{d} (Polynomial kernel)

Can be inner product in infinite dimensional space
Assume x ∈ R^{1} and γ > 0.

e^{−γkx}^{i}^{−x}^{j}^{k}^{2} = e^{−γ(x}^{i}^{−x}^{j}^{)}^{2} = e^{−γx}^{i}^{2}^{+2γx}^{i}^{x}^{j}^{−γx}^{j}^{2}

=e^{−γx}^{i}^{2}^{−γx}^{j}^{2} 1 + 2γx_{i}x_{j}

1! + (2γx_{i}x_{j})^{2}

2! + (2γx_{i}x_{j})^{3}

3! + · · ·

=e^{−γx}^{i}^{2}^{−γx}^{j}^{2} 1 · 1+

r2γ
1!x_{i} ·

r2γ
1!x_{j}+

r(2γ)^{2}
2! x_{i}^{2} ·

r(2γ)^{2}
2! x_{j}^{2}
+

r(2γ)^{3}
3! x_{i}^{3} ·

r(2γ)^{3}

3! x_{j}^{3} + · · · = φ(x_{i})^{T}φ(xj),
where

φ(x ) = e^{−γx}^{2}

1,

r2γ 1!x ,

r(2γ)^{2}
2! x^{2},

r(2γ)^{3}

3! x^{3}, · · ·

T

.

### Issues

So what kind of kernel should I use?

What kind of functions are valid kernels?

How to decide kernel parameters?

Some of these issues will be discussed later

### Decision function

At optimum

w =Pl

i =1α_{i}y_{i}φ(x_{i})
Decision function

w^{T}φ(x) + b

=

l

X

i =1

α_{i}y_{i}φ(x_{i})^{T}φ(x) + b

=

l

X

i =1

α_{i}y_{i}K (x_{i}, x) + b

Only φ(x_{i}) of α_{i} > 0 used ⇒ support vectors

### Support Vectors: More Important Data

Only φ(x_{i}) of α_{i} > 0 used ⇒ support vectors

-0.2 0 0.2 0.4 0.6 0.8 1 1.2

-1.5 -1 -0.5 0 0.5 1

### Outline

Basic concepts: SVM and kernels Training SVM

Practical use of SVM

Research directions: large-scale training Research directions: linear SVM

Research directions: others Conclusions

### Large Dense Quadratic Programming

minα

1

2α^{T}Qα − e^{T}α

subject to 0 ≤ α_{i} ≤ C , i = 1, . . . , l
y^{T}α = 0

Q_{ij} 6= 0, Q : an l by l fully dense matrix
50,000 training points: 50,000 variables:

(50, 000^{2} × 8/2) bytes = 10GB RAM to store Q
Traditional optimization methods:

Newton, quasi Newton cannot be directly applied

### Decomposition Methods

Working on some variables each time (e.g., Osuna et al., 1997; Joachims, 1998; Platt, 1998)

Similar to coordinate-wise minimization Working set B , N = {1, . . . , l }\B fixed Sub-problem at the kth iteration:

minαB

1

2α^{T}_{B} (α^{k}_{N})^{T}Q_{BB} QBN

Q_{NB} Q_{NN}

α_{B}
α^{k}_{N}

−

e^{T}_{B} (e^{k}_{N})^{T}α_{B}
α^{k}_{N}

subject to 0 ≤ α_{t} ≤ C , t ∈ B, y_{B}^{T}α_{B} = −y^{T}_{N}α^{k}_{N}

### Avoid Memory Problems

The new objective function 1

2α^{T}_{B}Q_{BB}α_{B} + (−e_{B} + Q_{BN}α^{k}_{N})^{T}α_{B} + constant
Only B columns of Q needed (|B| ≥ 2)

Calculated when used Trade time for space

### How Decomposition Methods Perform?

Convergence not very fast

But, no need to have very accurate α Prediction not affected much

In some situations, # support vectors # training points

Initial α^{1} = 0, some instances never used

An example of training 50,000 instances using LIBSVM

$svm-train -c 16 -g 4 -m 400 22features Total nSV = 3370

Time 79.524s

On a Xeon 2.0G machine

Calculating the whole Q takes more time

#SVs = 3,370 50,000

A good case where some remain at zero all the time

### Issues of Decomposition Methods

Techniques for faster decomposition methods store recently used kernel elements working set size/selection

theoretical issues: convergence

and others (details not discussed here) Major software:

LIBSVM

http://www.csie.ntu.edu.tw/~cjlin/libsvm
SVM^{light}

http://svmlight.joachims.org

### Outline

Basic concepts: SVM and kernels Training SVM

Practical use of SVM

Research directions: large-scale training Research directions: linear SVM

Research directions: others Conclusions

### Let’s Try a Practical Example

A problem from astroparticle physics

1 1:2.61e+01 2:5.88e+01 3:-1.89e-01 4:1.25e+02 1 1:5.70e+01 2:2.21e+02 3:8.60e-02 4:1.22e+02 1 1:1.72e+01 2:1.73e+02 3:-1.29e-01 4:1.25e+02 1 1:2.17e+01 2:1.24e+02 3:1.53e-01 4:1.52e+02 1 1:9.13e+01 2:2.93e+02 3:1.42e-01 4:1.60e+02 1 1:5.53e+01 2:1.79e+02 3:1.65e-01 4:1.11e+02 1 1:2.95e+01 2:1.91e+02 3:9.90e-02 4:1.03e+02 Training and testing sets available: 3,089 and 4,000

Sparse format: zero values not stored

### Poor Results from Direct Training/Testing

Training

$./svm-train train.1

optimization finished, #iter = 6131 nSV = 3053, nBSV = 724

Total nSV = 3053 Testing

$./svm-predict test.1 train.1.model test.1.out Accuracy = 66.925% (2677/4000)

nSV and nBSV: number of SVs and bounded SVs
(α_{i} = C ).

### Why this Fails

After training, nearly 100% support vectors Training and testing accuracy different

$./svm-predict train.1 train.1.model o Accuracy = 99.7734% (3082/3089)

Most kernel elements:

K_{ij} = e^{−kx}^{i}^{−x}^{j}^{k}^{2}^{/4}
(

= 1 if i = j ,

→ 0 if i 6= j . Some features in rather large ranges

### Data Scaling

Without scaling

Attributes in greater numeric ranges may dominate Linearly scale the first to [0, 1] by:

feature value − min max − min , There are other ways

Scaling generally helps, but not always

### Data Scaling: Same Factors

A common mistake

$./svm-scale -l -1 -u 1 train.1 > train.1.scale

$./svm-scale -l -1 -u 1 test.1 > test.1.scale Same factor on training and testing

$./svm-scale -s range1 train.1 > train.1.scale

$./svm-scale -r range1 test.1 > test.1.scale

### After Data Scaling

Train scaled data and then predict

$./svm-train train.1.scale

$./svm-predict test.1.scale train.1.scale.model test.1.predict

Accuracy = 96.15%

Training accuracy now is

$./svm-predict train.1.scale train.1.scale.model o Accuracy = 96.439%

Default parameter: C = 1, γ = 0.25

### Different Parameters

If we use C = 20, γ = 400

$./svm-train -c 20 -g 400 train.1.scale

$./svm-predict train.1.scale train.1.scale.model o Accuracy = 100% (3089/3089)

100% training accuracy but

$./svm-predict test.1.scale train.1.scale.model o Accuracy = 82.7% (3308/4000)

Very bad test accuracy Overfitting happens

### Overfitting

In theory

You can easily achieve 100% training accuracy This is useless

When training and predicting a data, we should Avoid underfitting: small training error

Avoid overfitting: small testing error

### l and s: training; and 4: testing

### Parameter Selection

Need to select suitable parameters C and kernel parameters

Example:

γ of e^{−γkx}^{i}^{−x}^{j}^{k}^{2}
a, b, d of (x^{T}_{i} xj/a + b)^{d}
How to select them?

So performance better?

### Performance Evaluation

Available data ⇒ training and validation Train the training; test the validation k-fold cross validation (CV):

Data randomly separated to k groups

Each time k − 1 as training and one as testing Select parameters/kernels with best CV result

### Selecting Kernels

RBF, polynomial, or others?

For beginners, use RBF first

Linear kernel: special case of RBF

Performance of linear the same as RBF under certain parameters (Keerthi and Lin, 2003) Polynomial: numerical difficulties

(< 1)^{d} → 0, (> 1)^{d} → ∞
More parameters than RBF

### A Simple Procedure

1. Conduct simple scaling on the data
2. Consider RBF kernel K (x, y) = e^{−γkx−yk}^{2}

3. Use cross-validation to find the best parameter C and γ

4. Use the best C and γ to train the whole training set 5. Test

For beginners only, you can do a lot more

### Contour of Parameter Selection

The good region of parameters is quite large SVM is sensitive to parameters, but not that sensitive

Sometimes default parameters work

but it’s good to select them if time is allowed

### Outline

Basic concepts: SVM and kernels Training SVM

Practical use of SVM

Research directions: large-scale training Research directions: linear SVM

Research directions: others Conclusions

### SVM doesn’t Scale Up

Yes, if using kernels

Training millions of data is time consuming

Cases with many support vectors: quadratic time bottleneck on

Q_{SV, SV}

For noisy data: # SVs increases linearly in data size (Steinwart, 2003)

Some solutions Parallelization Approximation

### Parallelization

Multi-core/Shared Memory/GPU

• One line change of LIBSVM

Multicore Shared-memory

1 80 1 100

2 48 2 57

4 32 4 36

8 27 8 28

50,000 data (kernel evaluations: 80% time)

• GPU (Catanzaro et al., 2008) Distributed Environments

• Chang et al. (2007); Zanni et al. (2006); Zhu et al.

(2009).

### Approximately Training SVM

Can be done in many aspects Data level: sub-sampling Optimization level:

Approximately solve the quadratic program Other non-intuitive but effective ways I will show one today

Many papers have addressed this issue

### Approximately Training SVM (Cont’d)

Subsampling

Simple and often effective More advanced techniques

Incremental training: (e.g., Syed et al., 1999)) Data ⇒ 10 parts

train 1st part ⇒ SVs, train SVs + 2nd part, . . . Select and train good points: KNN or heuristics For example, Bakır et al. (2005)

### Approximately Training SVM (Cont’d)

Approximate the kernel; e.g., Fine and Scheinberg (2001); Williams and Seeger (2001)

Use part of the kernel; e.g., Lee and Mangasarian (2001); Keerthi et al. (2006)

Early stopping of optimization algorithms Tsang et al. (2005) and others

And many more

Some simple but some sophisticated

### Approximately Training SVM (Cont’d)

Sophisticated techniques may not be always useful Sometimes slower than sub-sampling

covtype: 500k training and 80k testing rcv1: 550k training and 14k testing

covtype rcv1

Training size Accuracy Training size Accuracy

50k 92.5% 50k 97.2%

100k 95.3% 100k 97.4%

500k 98.2% 550k 97.8%

### Approximately Training SVM (Cont’d)

Sophisticated techniques may not be always useful Sometimes slower than sub-sampling

covtype: 500k training and 80k testing rcv1: 550k training and 14k testing

covtype rcv1

Training size Accuracy Training size Accuracy

50k 92.5% 50k 97.2%

100k 95.3% 100k 97.4%

500k 98.2% 550k 97.8%

### Discussion: Large-scale Training

We don’t have many large and well labeled sets Expensive to obtain true labels

Specific properties of data should be considered We will illustrate this point using linear SVM The design of software for very large data sets should be application different

### Outline

Basic concepts: SVM and kernels Training SVM

Practical use of SVM

Research directions: large-scale training Research directions: linear SVM

Research directions: others Conclusions

### Linear SVM

Data not mapped to another space

w,b,ξmin 1

2w^{T}w + C

l

X

i =1

ξi

subject to yi(w^{T}xi + b) ≥ 1 −ξi,
ξ_{i} ≥ 0, i = 1, . . . , l .

In theory, RBF kernel with certain parameters ⇒ as good as linear (Keerthi and Lin, 2003):

Test accuracy of linear ≤ Test accuracy of RBF But can be an approximation to nonlinear

Recently linear SVM an important research topic

### Linear SVM for Large Document Sets

Bag of words model (TF-IDF or others) A large # of features

Accuracy similar with/without mapping vectors What if training is much faster?

A very effective approximation to nonlinear SVM

### A Comparison: LIBSVM and LIBLINEAR

rcv1: # data: > 600k, # features: > 40k Using LIBSVM (linear kernel): > 10 hours Using LIBLINEAR (same stopping condition) Computation: < 5 seconds; I/O: 60 seconds Accuracy similar to nonlinear; more than 100x speedup

Training millions of data in a few seconds

See some results in Hsieh et al. (2008) by running LIBLINEAR

http:

//www.csie.ntu.edu.tw/~cjlin/liblinear

### Testing Accuracy versus Training Time

news20 yahoo-japan

rcv1 yahoo-korea

### Why Training Linear SVM Is Faster?

In optimization, each iteration often needs

∇_{i}f (α) = (Qα)i − 1
Nonlinear SVM

∇_{i}f (α) = X^{l}

j =1y_{i}y_{j}K (x_{i}, x_{j})α_{j} − 1
cost: O(nl ); n: # features, l : # data

Linear: use w ≡ Xl

j =1y_{j}α_{j}x_{j} and ∇_{i}f (α) = y_{i}w^{T}x_{i} − 1
Only O(n) cost if w is maintained

### Extension: Training Explicit Form of Nonlinear Mappings

Linear-SVM method to train φ(x_{1}), . . . , φ(x_{l})
Kernel not used

Applicable only if dimension of φ(x) not too large Low-degree Polynomial Mappings

K (x_{i}, x_{j}) = (x^{T}_{i} x_{j} + 1)^{2} = φ(x_{i})^{T}φ(x_{j})
φ(x) = [1,√

2x_{1}, . . . ,√

2x_{n}, x_{1}^{2}, . . . , x_{n}^{2},

√

2x1x2, . . . ,

√

2xn−1xn]^{T}

When degree is small, train the explicit form of φ(x)

### Testing Accuracy and Training Time

Data set

Degree-2 Polynomial Accuracy diff.

Training time (s)

Accuracy Linear RBF LIBLINEAR LIBSVM

a9a 1.6 89.8 85.06 0.07 0.02

real-sim 59.8 1,220.5 98.00 0.49 0.10

ijcnn1 10.7 64.2 97.84 5.63 −0.85

MNIST38 8.6 18.4 99.29 2.47 −0.40

covtype 5,211.9 NA 80.09 3.74 −15.98

webspam 3,228.1 NA 98.44 5.29 −0.76

Training φ(x_{i}) by linear: faster than kernel, but
sometimes competitive accuracy

### Discussion: Directly Train φ(x

_{i}

### ), ∀i

See details in our work (Chang et al., 2010) A related development: Sonnenburg and Franc (2010)

Useful for certain applications

### Linear Classification: Data Larger than Memory

Existing methods cannot easily handle this situation See our recent KDD work (Yu et al., 2010)

KDD 2010 best paper award

Training several million data (or more) on your laptop

### Linear Classification: Online Learning

For extremely large data, cannot keep all data

After using new data to update the model; may not need them any more

Online learning instead of offline learning

Training often by stochastic gradient descent methods

They use only a subset of data at each step Now an important research topic (e.g.,

Shalev-Shwartz et al., 2007; Langford et al., 2009;

Bordes et al., 2009)

### Linear Classification: L1 Regularization

1-norm versus 2-norm

kwk_{1} = |w_{1}| + · · · + |w_{n}|, kwk^{2}_{2} = w_{1}^{2} + · · · + w_{n}^{2}

w

|w |

w
w^{2}

2-norm: all wi are non-zeros; 1-norm: some wi may be zeros; useful for feature selection

Recently a hot topic; see our survey (Yuan et al., 2010)

### Outline

Basic concepts: SVM and kernels Training SVM

Practical use of SVM

Research directions: large-scale training Research directions: linear SVM

Research directions: others Conclusions

### Multiple Kernel Learning (MKL)

How about using

t_{1}K_{1} + t_{2}K_{2} + · · · + t_{r}K_{r}, where t_{1} + · · · + t_{r} = 1
as the kernel

Related to parameter/kernel selection

If K_{1} better ⇒ t_{1} close to 1, others close to 0
Earlier development (Lanckriet et al., 2004): high
computational cost

Many subsequent works (e.g., Rakotomamonjy et al., 2008).

Still ongoing; so far MKL has not been a practical tool yet

### Ranking

Labels become ranking information e.g., x1 ranks higher than x2

RankSVM (Joachims, 2002): add constraint
w^{T}x_{i} ≥ w^{T}x_{j} + ξ_{ij} if x_{i} ranks better than x_{j}
Many subsequent works

However, whether SVM is the most suitable method for ranking is an issue

### Other Directions

Semi-supervised learning

Use information from unlabeled data Active learning

Needs cost to obtain labels of data Cost sensitive learning

For unbalanced data Structured Learning

Data instance not an Euclidean vector Maybe a parse tree of a sentence Feature selection

### Outline

Basic concepts: SVM and kernels Training SVM

Practical use of SVM

Research directions: large-scale training Research directions: linear SVM

Research directions: others Conclusions

### Discussion and Conclusions

SVM and kernel methods are rather mature areas But still quite a few interesting research issues Many are extensions of standard classification (e.g., semi-supervised learning)

It is possible to identify more extensions through real applications

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