4 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…
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…