1 research outputs found
Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection
The Minimal Learning Machine (MLM) is a nonlinear supervised approach based
on learning a linear mapping between distance matrices computed in the input
and output data spaces, where distances are calculated using a subset of points
called reference points. Its simple formulation has attracted several recent
works on extensions and applications. In this paper, we aim to address some
open questions related to the MLM. First, we detail theoretical aspects that
assure the interpolation and universal approximation capabilities of the MLM,
which were previously only empirically verified. Second, we identify the task
of selecting reference points as having major importance for the MLM's
generalization capability. Several clustering-based methods for reference point
selection in regression scenarios are then proposed and analyzed. Based on an
extensive empirical evaluation, we conclude that the evaluated methods are both
scalable and useful. Specifically, for a small number of reference points, the
clustering-based methods outperformed the standard random selection of the
original MLM formulation.Comment: 29 pages, Accepted to JML