# Data normalization in k-means and svm

Generally if I want to normalize my data in which direction I should normalize (subtracting mean and dividing by std)?

Lets say I have a data matrix D (dxN, d-dimension of data, N - no of data) where each column represents a feature vector obtained from an image. In that case should I apply a column wise normalization (independently for each image) or should I normalize each feature type independently?

If I am correct, in k-means a column-wise normalization is applied but in SVMs a row-wise normalization is used. Why is that?