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cs.LG2026
Maximum-distance nonnegative matrix factorization for unmixing highly mixed grain-size distribution data
Qianqian Qi, Zhongming Chen, Peter G. M. van der Heijden
Unmixing grain-size distribution data into several end-members can help reveal sediment provenance, transport, and sedimentation processes. Traditional end-member unmixing methods…
cs.LG2026
Identification of NMF by choosing maximum-volume basis vectors
Qianqian Qi, Zhongming Chen, Peter G. M. van der Heijden
In nonnegative matrix factorization (NMF), minimum-volume-constrained NMF is a widely used framework for identifying the solution of NMF by making basis vectors as similar as possi…
stat.ME2026
Unmixing highly mixed grain size distribution data via maximum volume constrained end member analysis
Qianqian Qi, Zhongming Chen, Peter G. M. van der Heijden
End member analysis (EMA) unmixes grain size distribution (GSD) data into a mixture of end members (EMs), thus helping understand sediment provenance and depositional regimes and p…