Multivariate receptor modeling by N-dimensional edge detection

By R. Henry. 2003

262 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/9756c0a4de21874e1788eabcf10f4fb9fb673542

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Philip K. Hopke
2. Robert M. Flight
3. Xavier Monfort Querol
4. Peter D. Wentzell
5. Romà Tauler
6. A. Alastuey
7. Mar Viana

In Information Systems

1. Philip K. Hopke
2. Robert M. Flight
3. Xavier Monfort Querol
4. Peter D. Wentzell
5. Romà Tauler
6. A. Alastuey
7. Mar Viana

Citations in the corpus

• Comparison of the results obtained by four receptor modelling methods in aerosol source apportionment studies (2009, methodology): “…are principal component analysis, PCA (Jolliffe, 2002), singular value decomposition, SVD (Golub and Van Loan, 1989), the UNMIX (Henry and Kim, 1990; Henry, 2003) and positive matrix factorization, PMF (Paatero and Tapper, 1994), which have already proven their reliability in a vast number of ler).”
• Review of receptor modeling methods for source apportionment (2016, methodology): “It is based on an eigenvector decomposition of the ambient data as the basis for defining the feasible region of the solution and defining the edges (Henry, 2003).”