Support vectors are those data points that form the support vector, which are the two vectors (in higher dimension) which separate the data. So, if your data contains negative points, the support vectors may also be negative. Also note that preprocessing such as standardization changes input values, which can be seen from following example,
rng("default");
X = rand(100,1);
y = rand(100,1);
mdl = fitrsvm(X,y,'Standardize', true);
% These are the actual support vectors
supportVectors1 = mdl.SupportVectors;
% These are not the support vectors, but the data points which correspond to them
supportvectors2 = mdl.X(mdl.IsSupportVector);
sum(abs(supportvectors2 - supportVectors1)) % This is non zero if Standardize=true
