Would changing the dimension space in knn classifier make space for more memory?

Would changing the dimension space in knn classifier make space for more memory? The LDA I was using could not contain more than 6400x21. My data set is 170884x21. Any advice?

Code:

function D = distfun(Train, Test, dist)

%DISTFUN Calculate distances from training points to test points.

[n,p] = size(Train);

D = zeros(n,size(Test,1));

numTest = size(Test,1);

switch dist

    case 'sqeuclidean'

        for i = 1:numTest

            D(:,i) = sum((Train - Test(repmat(i,n,1),:)).^2, 2);

        end

    case 'cityblock'

        for i = 1:numTest

            D(:,i) = sum(abs(Train - Test(repmat(i,n,1),:)), 2);

        end

    case {'cosine','correlation'}

        % Normalized both the training and test data.

        normTrain = sqrt(sum(Train.^2, 2));

        normTest = sqrt(sum(Test.^2, 2));

        normData = sqrt(sum([Train;Test].^2, 2));

        Train = Train ./ normTrain(:,ones(1,size(Train,2)));

        if any(normData < eps) % small relative to unit-length data points

            error('stats:knn:ZeroTestentroid', ...

                'Zero cluster centroid created at iteration %d.',iter);

        end

        % This can be done without a loop, but the loop saves memory

allocations

        for i = 1:numTest

            D(:,i) = 1 - (Train * Test(i,:)') ./ normTest(i);

        end

I tried changing the line, D = zeros(n,size(Test,1)); to D = zeros(n,size(Test,21)); will it help?