cs558 project local svm classification based on triangulation (on the plane) glenn fung
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CS558 Project
Local SVM Classification based on triangulation
(on the plane)
Glenn Fung
Outline of Talk
Classification problem on the plane All of the recommended stages were applied:
Sampling Ordering:
Clustering Triangulation
Interpolation (Classification)SVM: Support vector Machines
Optimization: Number of training points increased Evaluation:
Checkerboard datasetSpiral dataset
Classification Problem in
Given m points in 2 dimensional space Represented by an m-by-2 matrix A Membership of each in class +1 or –1A i
R 2
SAMPLING:
1000 randomly sampled points
ORDERING:
Clustering A Fuzzy-logic based clustering algorithm was used. 32 cluster centers were obtained
-50 0 50 100 150 200 250-50
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0 50 100 150 200
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ORDERING:
Delaunay Triangulation Algorithms to triangulate and to get the Delaunay triangulation from HWKs 3 and 4 were used. Given a point,the random point approach is used to localize the triangle that contains it.
Interpolation:
SVM SVM : Support Vector Machine Classifiers A different nonlinear Classifier is used for each triangle
The triangle structure is efficiently used for both training and testing phases and for defining a “simple” and fast nonlinear classifier.
What is a Support Vector Machine?
An optimally defined surface Typically nonlinear in the input space Linear in a higher dimensional space Implicitly defined by a kernel function
What are Support Vector Machines Used For?
Classification Regression & Data Fitting Supervised & Unsupervised Learning
(Will concentrate on classification)
Support Vector MachinesMaximizing the Margin between Bounding
Planes
x0w= í +1
x0w= í à 1
A+
A-
jjwjj22
w
The Nonlinear Classifier
K (A;A0) : Rmân â Rnâm7à! Rmâm
K (x0;A0)Du = í
The nonlinear classifier:
Where K is a nonlinear kernel, e.g.: Gaussian (Radial Basis) Kernel :
"àökA iàA jk22; i; j = 1;. . .;mK (A;A0)ij =
The ij -entry of K (A;A0) represents the “similarity” of data points A i A jand
Reduced Support Vector Machine AlgorithmNonlinear Separating Surface: K (x0;Aö0)Döuö= í
(i) Choose a random subset matrix ofA 2 Rmân
entire data matrix A 2 Rmân
(ii) Solve the following problem by the Newtonmethod with corresponding D ú D :
2÷kp(eà D(K (A;A0)Döuöà eí );ë)k22+ 2
1kuö; í k22min(u; í ) 2 Rm+1
K (x0;Aö0)Döuö= í
(iii) The separating surface is defined by the optimal(u;í )solution in step (ii):
How to Choose in RSVM?A
A is a representative sample of the entire dataset Need not be a subset of A
A good selection of A may generate a classifier usingvery small m
Possible ways to chooseA :
Choose random rows from the entire datasetm A Choose such that the distance between its rows A
exceeds a certain tolerance Use k cluster centers of Aas AàA+ and
Obtained Bizarre “Checkerboard”
Optimization: More sampled pointsTraining parameters adjusted
Result: Improved Checkerboard
Nonlinear PSVM: Spiral Dataset94 Red Dots & 94 White Dots
Next:Bascom Hill
Some Questions
Would it work for B&W pictures (regression instead of classification?
Aplications?
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