Single image feature reduction at inference time : SVM
Just like all other Class, you have  different methods i.e.fit, transform, fit_transform

fit(self, X[, y]) Fit the model with X.
fit_transform(self, X[, y]) - Fit the model with X and apply the dimensionality reduction on X.
transform(self, X) - Apply dimensionality reduction to X. X is projected on the first principal components previously extracted from a training set.

So, you fit on the data received until the current data and then use transform on the current data.
It's similar to the way we will do Scaling in online learning e.g. You fit your StandardScaler on train data and transform the new data on that.