# Is there a representer theorem for unsupervised learning (to justify kernel density estimation)?

In supervised learning, we get a representer theorem by considering regularized losses of the following form:

In Kernel Density Estimation, we simply directly assume densities of the form

Could this be justified by for example regularized maximum likelihood? In other words, is there a representer theorem for unsupervised learning?