MINBALL | ![]() |
Minimal enclosing ball in kernel feature space.
Synopsis:
model = minball(X)
model = minball(X,options)
Description:
It computes center and radius of the minimal ball
enclosing data X mapped into a feature space induced
by a given kernel. The problem leads to a QP problem which is
solve by 'quadprog' of the MATLAB Optimization toolbox.
Input:
X [dim x num_data] Input data.
options [struct] Control parameters:
.ker [string] Kernel identifier (default 'linear'). See 'help kernel'.
.arg [1 x nargs] Kernel arguments.
.eps [1x1] Multipliers less then eps are set to zero (default 1e-12).
.mu [1x1] Regularization constant given to diagonal of the
kernel matrix (default 1e-12).
Output:
model [struct] Center of the ball in the kernel feature space:
.sv.X [dim x nsv] Data determining the center.
.Alpha [nsv x 1] Data weights.
.r [1x1] Radius of the minimal enclosing ball.
.b [1x1] Squared norm of the center equal to Alpha'*K*Alpha.
.options [struct] Copy of used options.
Example:
data = load('riply_trn');
options = struct('ker','linear','arg',1);
model = minball(data.X,options);
[Ax,Ay] = meshgrid(linspace(-5,5,100),linspace(-5,5,100));
dist = kdist([Ax(:)';Ay(:)'],model);
figure; hold on;
ppatterns(data.X); ppatterns(model.sv.X,'ro',12);
contour( Ax, Ay, reshape(dist,100,100),[model.r model.r]);
See also
KDIST.
About: Statistical Pattern Recognition Toolbox
(C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac
Czech Technical University Prague
Faculty of Electrical Engineering
Center for Machine Perception
Modifications:
25-aug-2004, VF, added model.fun = 'kdist' and .diag_add changed to .mu
16-may-2004, VF
15-jun-2002, VF