Start with using loops at first:
data = randi([0, 1], [2, 10, 112, 240, 30]); % Some test data
for i3 = 1:size(data, 3)
for i4 = 1:size(data, 4)
for i5 = 1:size(data, 5)
if ~any(data(:, :, i3, i4, i5))
% mark this submatrix for deleting
% (by the way: do not delete inside the loop, because this
% would shift the following indices!)
end
end
end
end
Now imagine, only the first submatrix contains only zeros: i3=1, i4=1, i5=1, and you want to delete this submatrix. Then the output array cannot have a rectangular shape anymore. This would be like a matrix, whose first row have one element less than the others.
So in the general case the wanted procedure is not possible.
Before you struggle with the smart and efficient vectorized solutions, writing dull loops is a good strategy to check, if 1. the procedure is working at all and 2. if the loop or the vectorized code is faster finally. Starting with smart optimized code is called "premature optimization" and a wide-spread programming anti-pattern.
