{"paper":{"title":"Monte Carlo Algorithm for Least Dependent Non-Negative Mixture Decomposition","license":"","headline":"","cross_cats":["cond-mat.stat-mech","cs.IT","math.IT","math.PR","math.ST","physics.comp-ph","physics.data-an","stat.TH"],"primary_cat":"physics.chem-ph","authors_text":"Alexander Kraskov, Harald St\\\"ogbauer, Peter Grassberger, Sergey A. Astakhov","submitted_at":"2006-01-20T21:25:45Z","abstract_excerpt":"We propose a simulated annealing algorithm (called SNICA for \"stochastic non-negative independent component analysis\") for blind decomposition of linear mixtures of non-negative sources with non-negative coefficients. The de-mixing is based on a Metropolis type Monte Carlo search for least dependent components, with the mutual information between recovered components as a cost function and their non-negativity as a hard constraint. Elementary moves are shears in two-dimensional subspaces and rotations in three-dimensional subspaces. The algorithm is geared at decomposing signals whose probabil"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"physics/0601161","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}