Matlab Leave-one-out Cross Validation for SVM

Hello & Assalamu'alaikum, I was train a 2592 data with 512 for it's feature. I wonder, is my code are correct?

Because of the training is need a long time for training process (~9 hours).

I am using Matlab's Toolbox named Classification Learner. Then i generate the code and i got a KFold Cross Validation as a default in generated code. Then i modifying it for Leave-one-out as below :

cvp = cvpartition(response, 'LeaveOut')
% Initialize the predictions and scores to the proper sizes
validationPredictions = response;
numObservations = size(predictors, 1);
numClasses = uniqueClassTotal;
validationScores = NaN(numObservations, numClasses);

% Loop Cross Validation dengan jumlah fold berdasarkan jumlah data
for fold = 1:allDataTotal
    trainingPredictors = predictors(cvp.training(fold), :);
    trainingResponse = response(cvp.training(fold), :);

      % Apply a PCA to the predictor matrix.
      % Convert all-numeric predictor table into matrix, ready for use by pca function.
      numericPredictors = table2array(varfun(@double, trainingPredictors));
      % 'inf' values have to be treated as missing data for PCA.
      numericPredictors(isinf(numericPredictors)) = NaN;
      [pcaCoefficients, pcaScores, ~, ~, explained, pcaCenters] = pca(...
          numericPredictors, ...
          'Centered', true);
      % Keep enough components to explain the desired amount of variance.
      explainedVarianceToKeepAsFraction = 90/100;
      numComponentsToKeep = find(cumsum(explained)/sum(explained) >= explainedVarianceToKeepAsFraction, 1);
      pcaCoefficients = pcaCoefficients(:,1:numComponentsToKeep);
      trainingPredictors = array2table(pcaScores(:,1:numComponentsToKeep));

      % Train a classifier
      % This code specifies all the classifier options and trains the classifier.
      % ambil nilai popup menu FungsiKernel
      nilaiFungsiKernel = get(handles.cbFungsiKernel,'String'); 
      FungsiKernel = nilaiFungsiKernel{get(handles.cbFungsiKernel,'Value')};

      template = templateSVM(...
          'KernelFunction', FungsiKernel, ...
          'Solver', 'SMO', ...
          'CacheSize', 'maximal', ...
          'Standardize', false);
      classificationSVM = fitcecoc(...
          trainingPredictors, ...
          trainingResponse, ...
          'Learners', template, ...
          'Coding', 'onevsall', ...
          'ClassNames', daftarKelas);

      pcaTransformationFcn = @(x) array2table(bsxfun(@minus, table2array(varfun(@double, x)), pcaCenters) * pcaCoefficients);
      svmPredictFcn = @(x) predict(classificationSVM, x);
      validationPredictFcn = @(x) svmPredictFcn(pcaTransformationFcn(x));

      % Compute validation predictions and scores
      validationPredictors = predictors(cvp.test(fold), :);

      [foldPredictions, foldScores] = validationPredictFcn(validationPredictors);

      % Store predictions and scores in the original order
      validationPredictions(cvp.test(fold), :) = foldPredictions;
      validationScores(cvp.test(fold), :) = foldScores;
  end

The total of fold loop is same as the total of data training. This is makes training process take a long time. Is my implementation of Leave-one-out for above code are correct?

Please help me, thanks in advance...

[mizuki]

If you have Parallel Computing Toolbox, use "parfor" instead of "for" for parallelization.