Learning using granularity statistical invariants for classification

arXiv:2403.20122v1 Announce Type: new
Abstract: Learning using statistical invariants (LUSI) is a new learning paradigm, which adopts weak convergence mechanism, and can be applied to a wider range of classification problems. However, the computation cost of invariant matrices in LUSI is high for large-scale datasets during training. To settle this issue, this paper introduces a granularity statistical invariant for LUSI, and develops a new learning paradigm called learning using granularity statistical invariants (LUGSI). LUGSI employs both strong and weak convergence …

abstract arxiv classification computation convergence cost cs.lg datasets however issue paper paradigm scale statistical training type