support vector machines - carnegie mellon school of...

Machine Learning

10-701, Fall 2015

Support Vector Machines

Eric Xing

Lecture 9, October 8, 2015

1

Reading: Chap. 6&7, C.B book, and listed papers© Eric Xing @ CMU, 2006-2015

2

What is a good Decision Boundary? Consider a binary classification

task with y = ±1 labels (not 0/1 as before).

When the training examples are linearly separable, we can set the parameters of a linear classifier so that all the training examples are classified correctly

Many decision boundaries! Generative classifiers Logistic regressions …

Are all decision boundaries equally good?

Class 1

Class 2

© Eric Xing @ CMU, 2006-2015

3

Not All Decision Boundaries Are Equal!

Why we may have such boundaries? Irregular distribution Imbalanced training sizes outliners

© Eric Xing @ CMU, 2006-2015

4

Classification and Margin Parameterzing decision boundary

Let w denote a vector orthogonal to the decision boundary, and b denote a scalar "offset" term, then we can write the decision boundary as:

0TT

T

wbx

ww

Class 1

Class 2

d - d+

© Eric Xing @ CMU, 2006-2015

5

Classification and Margin Parameterzing decision boundary

Let w denote a vector orthogonal to the decision boundary, and b denote a scalar "offset" term, then we can write the decision boundary as:

Class 1

Class 2

Margin

(wTxi+b)/||w|| > +c/||w|| for all xi in class 2(wTxi+b)/||w|| < c/||w|| for all xi in class 1

Or more compactly:

(wTxi+b)yi /||w|| >c/||w||

The margin between any two pointsm = d + d+ =

d - d+

0TT

T

wbx

ww

© Eric Xing @ CMU, 2006-2015

6

Maximum Margin Classification The minimum permissible margin is:

Here is our Maximum Margin Classification problem:

wcxx

wwm ji

T 2 **

iwcwbxwywc

iT

i

w

,//)(s.t

2max

© Eric Xing @ CMU, 2006-2015

7

Maximum Margin Classification, con'd. The optimization problem:

But note that the magnitude of c merely scales w and b, and does not change the classification boundary at all! (why?)

So we instead work on this cleaner problem:

The solution to this leads to the famous Support Vector Machines --- believed by many to be the best "off-the-shelf" supervised learning algorithm

icbxwy

wc

iT

i

bw

,)(s.tmax ,

ibxwy

w

iT

i

bw

,)(s.tmax ,

1

1

© Eric Xing @ CMU, 2006-2015

8

Support vector machine A convex quadratic programming problem

with linear constrains:

The attained margin is now given by

Only a few of the classification constraints are relevant support vectors

Constrained optimization We can directly solve this using commercial quadratic programming (QP) code But we want to take a more careful investigation of Lagrange duality, and the

solution of the above in its dual form. deeper insight: support vectors, kernels … more efficient algorithm

ibxwy

w

iT

i

bw

,)(s.tmax ,

1

1

w1

d+

d-

© Eric Xing @ CMU, 2006-2015

9

Digression to Lagrangian Duality The Primal Problem

Primal:

The generalized Lagrangian:

the 's (≥0) and 's are called the Lagarangian multipliers

Lemma:

A re-written Primal:

liwhkiwg

wf

i

i

w

,1, ,)(,1, ,)(

)(s.t.min

00

l

iii

k

iii whwgwfw

11)()()(),,( L

o/w

sconstraint primal satisfies if)(),,(max ,,

wwfw

i L0

),,(maxmin ,, w iw L0

© Eric Xing @ CMU, 2006-2015

10

Lagrangian Duality, cont. Recall the Primal Problem:

The Dual Problem:

Theorem (weak duality):

Theorem (strong duality):Iff there exist a saddle point of , we have

),,(maxmin ,, w iw L0

),,(minmax ,, wwiL0

*,,,,

* ),,(maxmin ),,(minmax pw wdii ww LL 00

** pd ),,( wL

© Eric Xing @ CMU, 2006-2015

11

A sketch of strong and weak duality Now, ignoring h(x) for simplicity, let's look at what's happening

graphically in the duality theorems.** )()(maxmin )()(minmax pwgw fwgwfd T

wT

w ii 00

)(wf

)(wg

© Eric Xing @ CMU, 2006-2015

12

A sketch of strong and weak duality Now, ignoring h(x) for simplicity, let's look at what's happening

graphically in the duality theorems.** )()(maxmin )()(minmax pwgw fwgwfd T

wT

w ii 00

)(wf

)(wg

© Eric Xing @ CMU, 2006-2015

13

The KKT conditions If there exists some saddle point of L, then the saddle point

satisfies the following "Karush-Kuhn-Tucker" (KKT) conditions:

Theorem: If w*, * and * satisfy the KKT condition, then it is also a solution to the primal and the dual problems.

mimiwgmiwgα

liw

kiww

i

i

ii

i

i

,,1 ,0,,1 ,0)(,,1 ,0)(

,,1 ,0 ),,(

,,1 ,0 ),,(

L

L

Primal feasibility

Dual feasibility

Complementary slackness

© Eric Xing @ CMU, 2006-2015

14

Solving optimal margin classifier Recall our opt problem:

This is equivalent to

Write the Lagrangian:

Recall that (*) can be reformulated asNow we solve its dual problem:

ibxwy

w

iT

i

bw

,)(s.tmax ,

1

1

ibxwy

ww

iT

i

Tbw

,)(s.tmin ,

0121

m

ii

Tii

T bxwywwbw1

121 )(),,( L

*( )

),,(maxmin , bw ibw L0

),,(minmax , bwbwiL0

© Eric Xing @ CMU, 2006-2015

15

***( )

The Dual Problem

We minimize L with respect to w and b first:

Note that (*) implies:

Plug (***) back to L , and using (**), we have:

),,(minmax , bwbwiL0

, ),,(

m

iiiiw xywbw

10L

, ),,(

m

iiib ybw

10L

m

iiii xyw

1

*( )

m

jij

Tijiji

m

ii yybw

11 21

,)(),,( xxL

**( )

m

ii

Tii

T bxwywwbw1

121 )(),,( L

© Eric Xing @ CMU, 2006-2015

16

The Dual problem, cont. Now we have the following dual opt problem:

This is, (again,) a quadratic programming problem. A global maximum of i can always be found. But what's the big deal?? Note two things:1. w can be recovered by

2. The "kernel"

m

jij

Tijiji

m

ii yy

11 21

,)()(max xx J

.

,, , s.t.

m

iii

i

y

ki

10

10

m

iiii yw

1x

jTi xx

See next …

More later …© Eric Xing @ CMU, 2006-2015

17

Support vectors Note the KKT condition --- only a few i's can be nonzero!!

miwgα ii ,,1 ,0)(

6=1.4

Class 1

Class 2

1=0.8

2=0

3=0

4=0

5=07=0

8=0.6

9=0

10=0Call the training data points whose i's are nonzero the support vectors (SV)

© Eric Xing @ CMU, 2006-2015

18

Support vector machines Once we have the Lagrange multipliers {i}, we can

reconstruct the parameter vector w as a weighted combination of the training examples:

For testing with a new data z Compute

and classify z as class 1 if the sum is positive, and class 2 otherwise

Note: w need not be formed explicitly

SVi

iii yw x

bzybzwSVi

Tiii

T

x

© Eric Xing @ CMU, 2006-2015

19

Interpretation of support vector machines

The optimal w is a linear combination of a small number of data points. This “sparse” representation can be viewed as data compression as in the construction of kNN classifier

To compute the weights {i}, and to use support vector machines we need to specify only the inner products (or kernel) between the examples

We make decisions by comparing each new example z with only the support vectors:

jTi xx

bzyySVi

Tiii xsign*

© Eric Xing @ CMU, 2006-2015

20

(1) Non-linearly Separable Problems

We allow “error” i in classification; it is based on the output of the discriminant function wTx+b

i approximates the number of misclassified samples

Class 1

Class 2

© Eric Xing @ CMU, 2006-2015

© Eric Xing @ CMU, 2006-2015 2121

(2) Non-linear Decision Boundary So far, we have only considered large-margin classifier with a

linear decision boundary How to generalize it to become nonlinear? Key idea: transform xi to a higher dimensional space to “make

life easier” Input space: the space the point xi are located Feature space: the space of (xi) after transformation

Why transform? Linear operation in the feature space is equivalent to non-linear operation in input

space Classification can become easier with a proper transformation. In the XOR

problem, for example, adding a new feature of x1x2 make the problem linearly separable (homework)

© Eric Xing @ CMU, 2006-2015 22

Non-linear Decision Boundary

22

© Eric Xing @ CMU, 2006-2015 2323

Transforming the Data

( )

( )

( )( )( )

( )

( )( )

(.) ( )

( )

( )( )( )

( )

( )

( )( ) ( )

Feature spaceInput spaceNote: feature space is of higher dimension than the input space in practice

© Eric Xing @ CMU, 2006-2015 2424

The Kernel Trick Recall the SVM optimization problem

The data points only appear as inner product As long as we can calculate the inner product in the feature

space, we do not need the mapping explicitly Many common geometric operations (angles, distances) can

be expressed by inner products Define the kernel function K by

m

jij

Tijiji

m

ii yy

11 21

,)()(max xx J

.0

,,1 ,0 s.t.

1

m

iii

i

y

miC

)()(),( jT

ijiK xxxx

© Eric Xing @ CMU, 2006-2015 2525

An Example for feature mapping and kernels Consider an input x=[x1,x2] Suppose (.) is given as follows

An inner product in the feature space is

So, if we define the kernel function as follows, there is no need to carry out (.) explicitly

2122

2121

2

1 2221 xxxxxxxx

,,,,,

''

,2

1

2

1

xx

xx

21 ')',( xxxx TK

© Eric Xing @ CMU, 2006-2015 2626

More examples of kernel functions Linear kernel (we've seen it)

Polynomial kernel (we just saw an example)

where p = 2, 3, … To get the feature vectors we concatenate all pth order polynomial terms of the components of x (weighted appropriately)

Radial basis kernel

In this case the feature space consists of functions and results in a non-parametric classifier.

')',( xxxx TK

pTK ')',( xxxx 1

2

21 'exp)',( xxxxK

© Eric Xing @ CMU, 2006-2015 2727

The essence of kernel Feature mapping, but “without paying a cost”

E.g., polynomial kernel

How many dimensions we’ve got in the new space? How many operations it takes to compute K()?

Kernel design, any principle? K(x,z) can be thought of as a similarity function between x and z This intuition can be well reflected in the following “Gaussian” function

(Similarly one can easily come up with other K() in the same spirit)

Is this necessarily lead to a “legal” kernel?(in the above particular case, K() is a legal one, do you know how many dimension (x) is?

© Eric Xing @ CMU, 2006-2015 2828

Kernel matrix Suppose for now that K is indeed a valid kernel corresponding

to some feature mapping , then for x1, …, xm, we can compute an mm matrix , where

This is called a kernel matrix!

Now, if a kernel function is indeed a valid kernel, and its elements are dot-product in the transformed feature space, it must satisfy: Symmetry K=KT

proof

Positive –semidefiniteproof?

32

Soft Margin Hyperplane Now we have a slightly different opt problem:

i are “slack variables” in optimization Note that i=0 if there is no error for xi

i is an upper bound of the number of errors C : tradeoff parameter between error and margin

, ,)(

s.ti

ibxwy

i

iiT

i

01

m

ii

Tbw Cww

121 ,min

© Eric Xing @ CMU, 2006-2015

33

(3) The Optimization Problem The dual of this new constrained optimization problem is

This is very similar to the optimization problem in the linear separable case, except that there is an upper bound C on i now

Once again, a QP solver can be used to find i

m

jij

Tijiji

m

ii yy

11 21

,)()(max xx J

.0

,,1 ,0 s.t.

1

m

iii

i

y

miC

© Eric Xing @ CMU, 2006-2015

34

The SMO algorithm Consider solving the unconstrained opt problem:

We’ve already see three opt algorithms! ? ? ?

Coordinate ascend:

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36

Sequential minimal optimization Constrained optimization:

Question: can we do coordinate along one direction at a time (i.e., hold all [-i] fixed, and update i?)

m

jij

Tijiji

m

ii yy

11 21

,)()(max xx J

.0

,,1 ,0 s.t.

1

m

iii

i

y

miC

© Eric Xing @ CMU, 2006-2015

37

The SMO algorithm

Repeat till convergence

1. Select some pair i and j to update next (using a heuristic that tries to pick the two that will allow us to make the biggest progress towards the global maximum).

2. Re-optimize J() with respect to i and j, while holding all the other k 's (k i; j) fixed.

Will this procedure converge?

© Eric Xing @ CMU, 2006-2015

38

Convergence of SMO

Let’s hold 3 ,…, m fixed and reopt J w.r.t. 1 and 2

m

jij

Tijiji

m

ii yy

1,1)(

21)(max xx J

.

,, ,0 s.t.

m

iii

i

y

kiC

10

1

KKT:

© Eric Xing @ CMU, 2006-2015

40

Cross-validation error of SVM The leave-one-out cross-validation error does not depend on

the dimensionality of the feature space but only on the # of support vectors!

examples trainingof #ctorssupport ve #error CVout -one-Leave

© Eric Xing @ CMU, 2006-2015

41

Summary Max-margin decision boundary

Constrained convex optimization

Duality

The KTT conditions and the support vectors

Non-separable case and slack variables

The kernel trick

The SMO algorithm

© Eric Xing @ CMU, 2006-2015

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