| KDIST | |
Computes distance between vectors in kernel space.
Synopsis:
d = kdist(X,model)
Description:
It computes distance between vectors mapped into the feature
space induced by the kernel function (model.options.ker,
model.options.arg). The distance is computed between images
of vectors X [dim x num_data] mapped into feature space
and a point in the feature space given by model:
d(i) = kernel(X(:,i),X(:,i))
- 2*kernel(X(:,i),models.sv.X)*model.Alpha + b,
where b [1x1] is assumed to be equal to
model.b = model.Alpha'*kernel(model.sv.X)*model.Alpha.
Input:
X [dim x num_data] Input vectors.
model [struct] Deternines a point of the feature space:
.Alpha [nsv x 1] Multipliers.
.sv.X [dim x nsv] Vectors.
.b [1x1] Bias.
.options.ker [string] Kernel identifier (see 'help kernel').
.options.arg [1 x nargs] Kernel argument(s).
Output:
d [num_data x 1] Distance between vectors in the feature space.
Example:
data = load('riply_trn');
model.Alpha = dualmean(size(data.X,2));
model.sv.X = data.X;
model.options.ker = 'rbf';
model.options.arg = 0.25;
model.b = model.Alpha'*kernel(data.X,'rbf',0.25)*model.Alpha;
[Ax,Ay] = meshgrid(linspace(-5,5,100), linspace(-5,5,100));
dist = kdist([Ax(:)';Ay(:)'],model);
figure; hold on;
ppatterns(data.X); contour( Ax, Ay, reshape(dist,100,100));
See also
MINBALL.
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, MINBALL added to See also
16-may-2004, VF
26-feb-2003, VF
13-sep-2002, VF
15-jun-2002, VF