@article{MTMT:34444849,
	title = {On problematic practice of using normalization in self-modeling/multivariate curve resolution (S/MCR)},
	url = {https://m2.mtmt.hu/api/publication/34444849},
	author = {Rajkó, Róbert},
	doi = {10.1016/j.chemolab.2023.105033},
	journal-iso = {CHEMOMETR INTELL LAB},
	journal = {CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS},
	volume = {244},
	unique-id = {34444849},
	issn = {0169-7439},
	abstract = {This short communication paper is briefly dealing with greater or lesser misused normalization in self-modeling/multivariate curve resolution (S/MCR) practice. The importance of the correct use of the ODE (ordinary differential equation) solvers and apt kinetic illustrations are elucidated. The new terms, external and internal normalizations are defined and interpreted. The problem of reducibility of a matrix is touched. Improper generalization/development of normalization-based methods are cited as examples. The position of the extreme values of the signal contribution function is clarified. An Executable Notebook with Matlab Live Editor was created for algorithmic explanations and depictions. © 2023 Elsevier B.V.},
	keywords = {Ordinary differential equations; Normalisation; Extreme value; rank deficiency; rank deficiency; Reducibility; Reducibility; Acceptance of a false proposition; Acceptance of a false proposition; External / Internal normalization; External / Internal normalization; Extreme values of the signal contribution function; Proper ODE solver; Extreme value of the signal contribution function; Ordinary differential equation (ODE) solvers; Proper ordinary differential equation solv; Signal contribution},
	year = {2024},
	eissn = {1873-3239},
	orcid-numbers = {Rajkó, Róbert/0000-0002-6234-658X}
}

@misc{MTMT:34092242,
	title = {On problematic practice of using normalization in Self-modeling/Multivariate Curve Resolution (S/MCR). with Supplementary Material},
	url = {https://m2.mtmt.hu/api/publication/34092242},
	author = {Rajkó, Róbert},
	unique-id = {34092242},
	year = {2023},
	orcid-numbers = {Rajkó, Róbert/0000-0002-6234-658X}
}

@misc{MTMT:34088704,
	title = {Bioinformatika - Biokemometria},
	url = {https://m2.mtmt.hu/api/publication/34088704},
	author = {Rajkó, Róbert},
	unique-id = {34088704},
	year = {2023},
	orcid-numbers = {Rajkó, Róbert/0000-0002-6234-658X}
}

@article{MTMT:33589010,
	title = {Partial Identifiability for Nonnegative Matrix Factorization},
	url = {https://m2.mtmt.hu/api/publication/33589010},
	author = {Gillis, Nicolas and Rajkó, Róbert},
	doi = {10.1137/22M1507553},
	journal-iso = {SIAM J MATRIX ANAL A},
	journal = {SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS},
	volume = {44},
	unique-id = {33589010},
	issn = {0895-4798},
	abstract = {Abstract. Given a nonnegative matrix factorization, \\(R\\) , and a factorization rank, \\(r\\) , exact nonnegative matrix factorization (exact NMF) decomposes \\(R\\) as the product of two nonnegative matrices, \\(C\\) and \\(S\\) with \\(r\\) columns, such as \\(R = CS^\\top\\) . A central research topic in the literature is the conditions under which such a decomposition is unique/identifiable up to trivial ambiguities. In this paper, we focus on partial identifiability, that is, the uniqueness of a subset of columns of \\(C\\) and \\(S\\) . We start our investigations with the data?based uniqueness (DBU) theorem from the chemometrics literature. The DBU theorem analyzes all feasible solutions of exact NMF and relies on sparsity conditions on \\(C\\) and \\(S\\) . We provide a mathematically rigorous theorem of a recently published restricted version of the DBU theorem, relying only on simple sparsity and algebraic conditions: it applies to a particular solution of exact NMF (as opposed to all feasible solutions) and allows us to guarantee the partial uniqueness of a single column of \\(C\\) or \\(S\\) . Second, based on a geometric interpretation of the restricted DBU theorem, we obtain a new partial identifiability result. This geometric interpretation also leads us to another partial identifiability result in the case \\(r=3\\) . Third, we show how partial identifiability results can be used sequentially to guarantee the identifiability of more columns of \\(C\\) and \\(S\\) . We illustrate these results on several examples, including one from the chemometrics literature.},
	year = {2023},
	eissn = {1095-7162},
	pages = {27-52},
	orcid-numbers = {Rajkó, Róbert/0000-0002-6234-658X}
}

@techreport{MTMT:32894534,
	title = {Partial Identifiability for Nonnegative Matrix Factorization},
	url = {https://m2.mtmt.hu/api/publication/32894534},
	author = {Gillis, Nicolas and Rajkó, Róbert},
	unique-id = {32894534},
	year = {2022},
	orcid-numbers = {Gillis, Nicolas/0000-0001-6423-6897; Rajkó, Róbert/0000-0002-6234-658X}
}

@article{MTMT:32646859,
	title = {Predicting the uniqueness of single non-negative profiles estimated by multivariate curve resolution methods},
	url = {https://m2.mtmt.hu/api/publication/32646859},
	author = {Akbari Lakeh, Mahsa and Abdollahi, Hamid and Rajkó, Róbert},
	doi = {10.1016/j.aca.2022.339575},
	journal-iso = {ANAL CHIM ACTA},
	journal = {ANALYTICA CHIMICA ACTA},
	volume = {1199},
	unique-id = {32646859},
	issn = {0003-2670},
	abstract = {In many kinds of chemical data, one or more species are unknown and the only efficient way to identify and/or quantify them is by mathematical resolution of the mixture spectra. The major problem with such mathematical decompositions is the possibility of obtaining a range of feasible solutions instead of a unique solution due to insufficient prior information about the system under study. However, even with the minimal non-negativity assumptions, there may be some levels of uniqueness, i.e., full/partial/fractional, in the results of the bilinear decomposition of chemical data which is very important to detect. In this study, a procedure is proposed to predict the uniqueness of the resolved non-negative profiles obtained by MCR-ALS (or analogous methods like NMF, EFA, SIMPLISMA, ITTFA, HELP, etc.). This uniqueness prediction is based on the data-based uniqueness (DBU) theorem and the general rule of uniqueness (GRU) presented in previous studies. The proposed procedure is easy to implement, has no additional computational cost, and is general for different systems with any number of components. Several simulated and experimental datasets containing different numbers of components were used to examine and evaluate the proposed procedure. © 2022 Elsevier B.V.},
	keywords = {PREDICTION; Computation theory; simulation; Forecasting; Feasible solution; Non-negativity; Multivariate curve resolution; Number of components; Priori information; resolution methods; Non negatives; Spectra's; Chemical data; Mathematical decomposition},
	year = {2022},
	eissn = {1873-4324},
	orcid-numbers = {Akbari Lakeh, Mahsa/0000-0002-1886-7468; Rajkó, Róbert/0000-0002-6234-658X}
}

@article{MTMT:31810109,
	title = {Közhasznúsági jelentés. a Magyar Kémikusok Egyesülete (MKE) 2019. évi közhasznú tevékenységéről és előterjesztés a 2020. évi terv főbb mutatóiról},
	url = {https://m2.mtmt.hu/api/publication/31810109},
	author = {Kovács, Attila and Csutorás, Csaba and Rajkó, Róbert and Sziva, Miklós and Wölfling, János},
	journal-iso = {MAGY KEM LAP},
	journal = {MAGYAR KÉMIKUSOK LAPJA},
	volume = {75},
	unique-id = {31810109},
	issn = {0025-0163},
	year = {2020},
	eissn = {1588-1199},
	pages = {323-330},
	orcid-numbers = {Rajkó, Róbert/0000-0002-6234-658X; Wölfling, János/0000-0002-3037-309X}
}

@techreport{MTMT:31749479,
	title = {Employing Partial Least Squares Regression with Discriminant Analysis for Bug Prediction},
	url = {https://m2.mtmt.hu/api/publication/31749479},
	author = {Ferenc, Rudolf and Siket, István and Hegedűs, Péter and Rajkó, Róbert},
	unique-id = {31749479},
	year = {2020},
	orcid-numbers = {Ferenc, Rudolf/0000-0001-8897-7403; Siket, István/0000-0003-4064-1489; Hegedűs, Péter/0000-0003-4592-6504; Rajkó, Róbert/0000-0002-6234-658X}
}

@{MTMT:31749456,
	title = {Data for: Employing Partial Least Squares Regression with Discriminant Analysis for Bug Prediction},
	url = {https://m2.mtmt.hu/api/publication/31749456},
	author = {Rajkó, Róbert and Ferenc, Rudolf and Siket, István and Hegedűs, Péter},
	doi = {10.17632/CB22T5225N.1},
	unique-id = {31749456},
	year = {2020},
	orcid-numbers = {Rajkó, Róbert/0000-0002-6234-658X; Ferenc, Rudolf/0000-0001-8897-7403; Siket, István/0000-0003-4064-1489; Hegedűs, Péter/0000-0003-4592-6504}
}

@CONFERENCE{MTMT:31630527,
	title = {Use of non-negative matrix factorization (NMF) for an exploratory analysis of atomic and molecular spectra of zooplankton},
	url = {https://m2.mtmt.hu/api/publication/31630527},
	author = {N, Sushkov and T, Labutin and N, Lobus and Galbács, Gábor and Rajkó, Róbert},
	booktitle = {Twelfth Winter Symposium on Chemometrics : Modern Methods of Data Analysis},
	unique-id = {31630527},
	year = {2020},
	pages = {48-49},
	orcid-numbers = {Galbács, Gábor/0000-0002-1799-5329; Rajkó, Róbert/0000-0002-6234-658X}
}