0000000000674607
AUTHOR
César L. C. Mattos
Sparse minimal learning machine using a diversity measure minimization
The minimal learning machine (MLM) training procedure consists in solving a linear system with multiple measurement vectors (MMV) created between the geometric congurations of points in the input and output spaces. Such geometric congurations are built upon two matrices created using subsets of input and output points, named reference points (RPs). The present paper considers an extension of the focal underdetermined system solver (FOCUSS) for MMV linear systems problems with additive noise, named regularized MMV FOCUSS (regularized M-FOCUSS), and evaluates it in the task of selecting input reference points for regression settings. Experiments were carried out using UCI datasets, where the …
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 generaliz…