Abstract. We discuss the existence of matrix representations for generalised and minimum participation constraints which are frequently used in database design and conceptual model...
Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been a...
Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can ...
We present algorithms for testing the satisfiability and finding the tightened transitive closure of conjunctions of addition constraints of the form ±x ± y ≤ d and bound co...
In this paper, we propose a novel method, called local nonnegative matrix factorization (LNMF), for learning spatially localized, parts-based subspace representation of visual pat...
Stan Z. Li, XinWen Hou, HongJiang Zhang, QianSheng...