Approximate Representer Theorems in Non-reflexive Banach Spaces
Kevin Schlegel

TL;DR
This paper extends the representer theorem to non-reflexive Banach spaces by introducing approximate solutions, enabling kernel methods in broader spaces like l1, which were previously intractable.
Contribution
It introduces the concept of approximate representer theorems in Banach spaces, broadening the applicability of kernel methods beyond Hilbert spaces.
Findings
Classical representer theorem cannot hold in non-reflexive Banach spaces.
Approximate representer theorems can be established in general Banach spaces.
Characterization of regularizers admitting classical or approximate theorems.
Abstract
The representer theorem is one of the most important mathematical foundations for regularised learning and kernel methods. Classical formulations of the theorem state sufficient conditions under which a regularisation problem on a Hilbert space admits a solution in the subspace spanned by the representers of the data points. This turns the problem into an equivalent optimisation problem in a finite dimensional space, making it computationally tractable. Moreover, Banach space methods for learning have been receiving more and more attention. Considering the representer theorem in Banach spaces is hence of increasing importance. Recently the question of the necessary condition for a representer theorem to hold in Hilbert spaces and certain Banach spaces has been considered. It has been shown that a classical representer theorem cannot exist in general in non-reflexive Banach spaces. In…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNumerical methods in inverse problems · Sparse and Compressive Sensing Techniques · Optimization and Variational Analysis
