Difference between revisions of "Lecture 12 - Support Vector Machine and Quadratic Optimization Problem OldKiwi" - Rhea

Revision as of 09:59, 20 March 2008

ECE662 Main Page

Class Lecture Notes

Support Vector Machines

(Continued from Lecture 11)

• Definition

The support vectors are the training points $ y_i $ such that $ \vec{c}\cdot{y_i}=b,\forall{i} $. i.e. they are the closest to the hyperplane.

• How to Train a Support Vector Machine (SVM)

We want to find a $ \vec{c} $ such that $ \vec{c}\cdot{y_i} \geq b, \forall{i} $. This however, is wishful thinking, so we try to find this for as many training samples as possible with $ b $ as large as possible.

Observe: If $ \vec{c} $ is a solution with margin $ b $, then $ \alpha\vec{c} $ is a solution with margin $ \alpha b, \forall{\alpha} \in \Re > 0 $

So to pose the problem well, we demand that $ \vec{c}\cdot{y_i} \geq 1, '\forall{i}' $ and try to minimize $ \vec{c} $

For small training sets, we can use a training method similar to online [perceptron]

   1) Pick a plane |c_vector|
   2) Find the worst classified sample.  |yi_0|  (Note: This step is computationally expensive for large data sets)
   3) Move plane |c_vector| to improve the classification
   4) Repeat steps 2-3 until the algorithm converges

.. |norm_c| image:: tex

  :alt: tex: ||\vec{c}||

.. |c_dot_yi_3| image:: tex

  :alt: tex: \vec{c}\cdot{y_i}=1,\forall{i}

.. |half_norm_c_sq| image:: tex

  :alt: tex: \frac{1}{2}||\vec{c}||^2

We want to minimize |norm_c| subject to the constraints |c_dot_yi_3| (i.e. correct classification). Note: Optimizing |norm_c| is hard, because the function is not convex. Instead, optimize |half_norm_c_sq| (convex function with the same optimum).

.. |c_yi_X| image:: tex

  :alt: tex: \vec{c}\cdot{y}_i\ge1,\forall{i}

.. |c_yi_SV| image:: tex

  :alt: tex: \vec{c}\cdot{y}_i=1,\forall

.. |yi| image:: tex

  :alt: tex: y_i

This gives a [Quadratic Optimization Problem]: Minimize |half_norm_c_sq| subject to the constraints |c_yi_X|. However, we should demand that |c_yi_SV| support vectors |yi|.

Formulate using [Lagrange multipliers]: (Someone should continue the notes from this point)

Karush-Kuhn-Tucker Construction

==========

Reformulate as "Dual Problem"

Maximize |khh1|, where i, j are indices of SV.

under constraint |khh2|, and |khh3|

.. |khh1| image:: tex

  :alt: tex: L(\alpha)=\displaystyle \sum_{i} \alpha_{i}-\frac{1}{2}\sum_{i,j} \alpha _i  \alpha_j y_i \cdot y_j

.. |khh2| image:: tex

  :alt: tex: \displaystyle \sum_{i=1}^{d_1}\alpha _i - \sum_{i=d+1}^{d} \alpha _i = 0

.. |khh3| image:: tex

  :alt: tex: \alpha_i \geq 0, \quad \forall{i}

then find |khh4|.

.. |khh4| image:: tex

  :alt: tex: \vec{c}=\sum_{i} \alpha_{i} y_i

Key points

+ L only depends on SV + L only depends on |khh5|, not |khh6| or |khh7| explicitly

.. |khh5| image:: tex

  :alt: tex: y_{i} \cdot y_{j}

.. |khh6| image:: tex

  :alt: tex: y_i 

.. |khh7| image:: tex

  :alt: tex: y_j

Recall: maximize |khh8|

.. |khh8| image:: tex

  :alt: tex: L(c,\vec{\alpha})=\frac{1}{2}||\vec{c}||^2 - \displaystyle \sum_{i} \alpha _i (\vec{c} \cdot y_i -1 )

When data is not linearly separable, max |khh9|, Do not try this numerial optimization.

.. |khh9| image:: tex

  :alt: tex: L(c,\vec{\alpha})=\infty

+ How to train a support vector machine using nonlinearly separable data. Define "soft margins" by introducing "slack variable" |khh9| , which measure misclassification of |khh6|.

.. |khh9| image:: tex

  :alt: tex: \xi_i

|khh10| and try to use as few non-zero as possible (use penalty term)

.. |khh10| image:: tex

  :alt: tex: \vec{c} \cdot y_i \geq 1 \Rightarrow \vec{c} \cdot y_i \geq 1 - \xi _i

Quadratic Optimization Problem

==========

minimize |khh11|

subject to |khh12|

.. |khh11| image:: tex

  :alt: tex: \frac{1}{2}||\vec{c}||^2 + constant \displaystyle \sum_{i=1}^{d} \xi _i

.. |khh12| image:: tex

  :alt: tex: \vec{c} \cdot y_i \geq 1 - \xi _i, \quad \forall{i}

The Kernel Trick

==

Proposed by Aizerman in another context used by Vapnik,Boser, Guyon to upgrade [SVM] to find non-linear decision boundary based on observation that [SVM] training only introduces |khh5|

|khh13|

.. |khh13| image:: tex

alt: tex: y_i \cdot y_j = \varphi (x_i) \cdot \varphi (x_j)

A kernel function |khh14| for a given mapping |khh15| is a function such that |khh16|.

.. |khh14| image:: tex

alt: tex: K:\Re ^k \times \Re ^k \rightarrow \Re

.. |khh15| image:: tex

alt: tex: \varphi

.. |khh16| image:: tex

alt: tex: K(x,x')=\varphi (x) \cdot \varphi (x')

Therefore, |khh17| -> No need to compute |khh18| and no need to know |khh15|

.. |khh17| image:: tex

alt: tex: y_i \cdot y_j = K(x_i,x_j)

.. |khh18| image:: tex

alt: tex: \varphi (x_i), \varphi (x_j)

In order to exploit kernel trick, we need to have good kernel functions. One way to do that is to choose |fx| (called basis functions) and use it to find corresponding kernel. Another method is to construct kernel functions directly by making sure that the function is a valid kernel which means it corresponds to a scalar product in some feature space. Necessary and sufficient condition for a function to be a valid kernel is Gram Matrix K whose elements are |kxx| to be positive semidefinite. Here are some figures to show the general characteristics of a kernel function.

.. |fx| image:: tex

alt: tex: \varphi (x)

.. |kxx| image:: tex

alt: tex: k(x_n, x_m)

.. image:: kernel.jpg

The left hand side figure represents the set of basis functions, and the right hand side represents the Gaussian Kernel Function.

A powerful method for creating good kernel functions is to use simpler kernel functions to build new ones. Because, we can manipulate kernel functions by using some transformations such as scalar multiplications, summing two kernel functions, inserting kernel functions to some other functions and so on.

2 Nice examples can be found in [Support Vector Machines : 1-D Example] and [SVM: 2-D Example]

One example of kernel functions

take |L13_1|

then, |L13_2|

|L13_2_2|

Here kernel is |L13_3|

Note : K is kernel for other functions |L13_4|

(for example |L13_5|

one-to-one correspondence between kernel and mapping |L13_4| s

.. |L13_1| image:: tex

alt: tex: \varphi (x_1 , x_2)=(x_1^2,\sqrt{2} x_1 x_2, x_2^2 )

.. |L13_2| image:: tex

alt: tex: \varphi (x_1 , x_2) \varphi (\acute{x_1} , \acute{x_2})=x_1^2 \acute{x_1^2}+2x_1 x_2 \acute{x_1} \acute{x_2} + x_2^2 \acute{x_2^2}

.. |L13_2_2| image:: tex

alt: tex: (x_1^2 \acute{x_1^2} + x_2^2 \acute{x_2^2} )^2 = [(x_1, x_2) (\acute{x_1^2}, \acute{x_2^2})]^2

.. |L13_3| image:: tex

alt: tex: K(\vec x, \acute{\vec x} ) = (\vec x, \acute{\vec x} )^2

.. |L13_4| image:: tex

alt: tex: \varphi

.. |L13_5| image:: tex

alt: tex: \varphi (x_1 , x_2) = \frac {1}{\sqrt{2}} (x_1^2 - x_2^2, 2x_1^2 x_2^2, x_1^2 + x_2^2)

Lectures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18