NCA feature selection method

i tried to use NCA feature selection method to select the most relitive features from 16483 features, but i always got all features because the bestlambda and bestloss are always 0. how can work in this problem.

another question how can i set the lambdavals, is there a rule or something for linspace () parameter? as well how can i select the best value for 'tol' ?

cvx=cvpartition(size(Features,1),'kfold',5);
numvalidsets = cvx.NumTestSets;
n = cvx.TrainSize(1);
lambdavals=(linspace(0,20,20)./n;
lossvals = zeros(length(lambdavals),numvalidsets);

for w = 1:length(lambdavals)
     for p =1:numvalidsets
         train=1;
         test=1;
         indextrain=training(cvx,p);
         for i=1:size(Features,1)
             if indextrain(i)==1
                 XTrain(train,:)=Features(i,:);
                 YTrain(train)=label(i);
                 train=train+1;
             else
                 XTest(test,:)=Features(i,:);
                 YTest(test)=label(i);
                 test=test+1;
             end
         end
         TrainData= XTrain,YTrain;
         TestData =XTest,YTest;

         nca = fscnca(XTrain,YTrain,'FitMethod','exact', ...
             'Solver','sgd','Lambda',lambdavals(w), ...
              'IterationLimit',1,'Standardize',true);
                  
        lossvals(w,p) = loss(nca,XTest,YTest,'LossFunction','classiferror');
     end
end
 %%


meanloss = mean(lossvals,2);

[~,idx] = min(meanloss) % Find the index
bestlambda = lambdavals(idx) % Find the best lambda value
bestloss = meanloss(idx)

nca = fscnca(XTrain,YTrain,'FitMethod','exact','Solver','sgd',...
    'Lambda',bestlambda,'Standardize',true,'Verbose',1);

tol    = 0.55;
selidx = find(nca.FeatureWeights > tol*max(1,max(nca.FeatureWeights)))
Best_Features_train = XTrain(:,selidx);

[Alexis]

I have this same problem. Without an error message or warning it's not clear to me where to start. I have 14 features and over 5,000 observations.