LDA analysis: The pooled covariance matrix of TRAINING must be positive definite.

5 visualizzazioni (ultimi 30 giorni)

Hello, I am running into this issue. How can I resolve it?

Y = csvread('mydata.csv');
flag = Y(:,1);
label = Y(:,2);
P = Y(:,3:end);
train = Y((flag < 5) & (label == 8|9),:);
test = Y((flag == 5) & (label == 0),:);
[coeff,score,latent] = pca(train);
group = Y((flag < 5) & (label == 8|9));
class = classify(Y,train,group,'linear');

My research online gives me some hints that I should apply PCA to the training samples and project onto the first 2 principal components. Then, apply LDA to project onto 1 dimension.

How can I take the result of PCA and input it as a parameter in classify()?

Thank you!

Risposte (1)

Fadi Alsuhimat
Fadi Alsuhimat il 6 Lug 2020
Just write it like this
augmentedTrainset=augmentedImageDatastore(imagesize,...
trainset,'ColorPreprocessing','gray2rgb');
%%% this mean you add another type for lda by using 'ColorPreprocessing','gray2rgb'

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by