Reproducing Kernel Banach Spaces with the ℓ1 Norm
Guohui Song, Haizhang Zhang, and Fred J. Hickernell
Targeting at sparse learning, we construct Banach spaces B of functions on an input space X with the properties that (1) B possesses an l1 norm in the sense that it is isometrically isomorphic to the Banach space of integrable functions on X with respect to the counting measure; (2) point evaluations are continuous linear functionals on B and are representable through a bilinear form with a kernel function; (3) regularized learning schemes on B satisfy the linear representer theorem. Examples of kernel functions admissible for the construction of such spaces are given.
This one probably requires some explanation, for the non-ML folks. Reproducing Kernel Hilbert spaces are the coin of the realm in machine learning and for good reason. They allow much of ML to be "ported" from linear classifiers to non-linear classifiers: the kernel mapping essentially linearizes (via lifting) the nonlinear classifiers so you can get the benefit of the nonlinearity while operating algorithmically in a linear world. Even though the induced Hilbert space is typically a function space and is therefore infinite-dimensional, the representer theorem allows us in most cases to operate in a finite dimensional space (where the dimension is bounded by the number of samples). From a metric embedding perspective, kernels completely characterize the class of metrics isometrically embeddable in Euclidean space.
So RKHSs are great. So what's the deal with this paper ? What it tries to do is combine the power of RKHSs with the regularity and sparsity properties guaranteed by $\ell_1$ norms. Even though your typical Banach space doesn't admit an inner product (what you need for the kernel mapping), they show that you can define special Banach spaces in which kernels can be defined as before, and the representer theorem holds, but that you can get sparse bases for solutions because of the nice $\ell_1$ properties.
I promised SHORT summaries, so I'm not going to go further. But the takeaway message here is the ability to extend the nice properties of RKHSs to Banach spaces. For completeness I should mention that there are other approaches that have tried to do this, but using different mathematical constructs that are in some way less well suited.