| LDA | |
Linear Discriminant Analysis.
Synopsis:
model = lda(data)
model = lda(data,new_dim)
Description:
This function is implementation of Linear Discriminant Analysis.
The goal is to train the linear transform which maximizes ratio
between between-class and within-class scatter matrix of projected
data.
Input:
data [struct] Input labeled data:
.X [dim x num_data] Data sample.
.y [1 x num_data] Labels (1,2,...,nclass).
new_dim [1x1] Output data dimension (default new_dim = dim).
Ouput:
model [struct] Linear projection:
.W [dim x new_dim] Projection matrix.
.b [new_dim x 1] Biases.
.mean_X [dim x 1] Mean value of data.
.Sw [dim x dim] Within-class scatter matrix.
.Sb [dim x dim] Between-class scatter matrix.
.eigval [dim x 1] Eigenvalues.
Example:
in_data = load('iris');
model = lda( in_data, 2 );
out_data = linproj( in_data, model);
figure; ppatterns(out_data);
See also
LINPROJ, PCA.
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:
05-oct-2006, VF
25-may-2004, VF
3-may-2004, VF
20-may-2001, V.Franc, created