New to machine learning and I was going over the topic of support vector machines. Can someone verify if I'm correct in saying that dual representations relate to support vectors in the way that if the weights over the training data is not equal to zero, we can then deduce it as a support vector and the fewer the support vectors there are, the more sparse the solution?
Thanks so much in advance.
The dual representation is the expression of a solution as a linear combination of training point locations (their actual location in input space if the kernel is linear; or their location in a high-dimensional feature space induced by the kernel, if non-linear). So the dual representation consists of a bunch of weights—one number corresponding to each data point. Those data points for which the corresponding weight is non-zero? yep, they're the support vectors.