6 papers
Maximum-distance nonnegative matrix factorization for unmixing highly mixed grain-size distribution data: A generalization of AnalySize
Qianqian Qi, Zhongming Chen, Peter G. M. van der Heijden
Nonnegative matrix factorization (NMF) decomposes a nonnegative matrix into the product of two nonnegative matrices. This property makes NMF well suited for unmixing grain-size dis…
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…
Correspondence Analysis and PMI-Based Word Embeddings: A Comparative Study
Qianqian Qi, Ayoub Bagheri, David J. Hessen +1
Popular word embedding methods such as GloVe and Word2Vec are related to the factorization of the pointwise mutual information (PMI) matrix. In this paper, we establish a formal co…
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…
A review of NMF, PLSA, LBA, EMA, and LCA with a focus on the identifiability issue
Qianqian Qi, Peter G. M. van der Heijden
Across fields such as machine learning, social science, geography, considerable attention has been given to models that factorize a nonnegative matrix into the product of two or th…
Correspondence analysis: handling cell-wise outliers via the reconstitution algorithm
Qianqian Qi, David J. Hessen, Aike N. Vonk +1
Correspondence analysis (CA) is a popular technique to visualize the relationship between two categorical variables. CA uses the data from a two-way contingency table and is affect…