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School of Computer Science University of Birmingham, UK
There is a long history of formulation of machine learning models that have at their core "representative prototypes" living in the data space (examples include variants of Self Organizing Maps in the unsupervised setting or variants of Learning Vector Quantization in the supervised one). The prototype based formulations make such models intuitive and naturally amenable to interpretation. I will present a series of generalizations and extensions of this idea, arguing that prototype based learning still deserves attention in the current era of deep learning.
In particular, I will present extensions of prototype based learning to model spaces and Riemannian manifolds. I will also show how prototype based models can be naturally extended to the setting of ordinal regression and learning with privileged information.
