TY - JOUR AU - Rajkó, Róbert TI - On problematic practice of using normalization in self-modeling/multivariate curve resolution (S/MCR) JF - CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS J2 - CHEMOMETR INTELL LAB VL - 244 PY - 2024 SN - 0169-7439 DO - 10.1016/j.chemolab.2023.105033 UR - https://m2.mtmt.hu/api/publication/34444849 ID - 34444849 N1 - Export Date: 20 December 2023; Cited By: 0; CODEN: CILSE AB - 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. LA - English DB - MTMT ER - TY - GEN AU - Rajkó, Róbert TI - On problematic practice of using normalization in Self-modeling/Multivariate Curve Resolution (S/MCR). with Supplementary Material TS - with Supplementary Material PY - 2023 PG - 63 UR - https://m2.mtmt.hu/api/publication/34092242 ID - 34092242 LA - English DB - MTMT ER - TY - GEN AU - Rajkó, Róbert TI - Bioinformatika - Biokemometria PY - 2023 UR - https://m2.mtmt.hu/api/publication/34088704 ID - 34088704 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Gillis, Nicolas AU - Rajkó, Róbert TI - Partial Identifiability for Nonnegative Matrix Factorization JF - SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS J2 - SIAM J MATRIX ANAL A VL - 44 PY - 2023 IS - 1 SP - 27 EP - 52 PG - 26 SN - 0895-4798 DO - 10.1137/22M1507553 UR - https://m2.mtmt.hu/api/publication/33589010 ID - 33589010 AB - 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. LA - English DB - MTMT ER - TY - GEN AU - Gillis, Nicolas AU - Rajkó, Róbert TI - Partial Identifiability for Nonnegative Matrix Factorization PY - 2022 PG - 26 UR - https://m2.mtmt.hu/api/publication/32894534 ID - 32894534 LA - English DB - MTMT ER - TY - JOUR AU - Akbari Lakeh, Mahsa AU - Abdollahi, Hamid AU - Rajkó, Róbert TI - Predicting the uniqueness of single non-negative profiles estimated by multivariate curve resolution methods JF - ANALYTICA CHIMICA ACTA J2 - ANAL CHIM ACTA VL - 1199 PY - 2022 PG - 10 SN - 0003-2670 DO - 10.1016/j.aca.2022.339575 UR - https://m2.mtmt.hu/api/publication/32646859 ID - 32646859 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Kovács, Attila AU - Csutorás, Csaba AU - Rajkó, Róbert AU - Sziva, Miklós AU - Wölfling, János TI - Közhasznúsági jelentés. a Magyar Kémikusok Egyesülete (MKE) 2019. évi közhasznú tevékenységéről és előterjesztés a 2020. évi terv főbb mutatóiról TS - a Magyar Kémikusok Egyesülete (MKE) 2019. évi közhasznú tevékenységéről és előterjesztés a 2020. évi terv főbb mutatóiról JF - MAGYAR KÉMIKUSOK LAPJA J2 - MAGY KEM LAP VL - 75 PY - 2020 IS - 11 SP - 323 EP - 330 PG - 8 SN - 0025-0163 UR - https://m2.mtmt.hu/api/publication/31810109 ID - 31810109 LA - Hungarian DB - MTMT ER - TY - GEN AU - Ferenc, Rudolf AU - Siket, István AU - Hegedűs, Péter AU - Rajkó, Róbert TI - Employing Partial Least Squares Regression with Discriminant Analysis for Bug Prediction PY - 2020 PG - 32 UR - https://m2.mtmt.hu/api/publication/31749479 ID - 31749479 LA - English DB - MTMT ER - TY - DATA AU - Rajkó, Róbert AU - Ferenc, Rudolf AU - Siket, István AU - Hegedűs, Péter TI - Data for: Employing Partial Least Squares Regression with Discriminant Analysis for Bug Prediction PY - 2020 DO - 10.17632/CB22T5225N.1 UR - https://m2.mtmt.hu/api/publication/31749456 ID - 31749456 N1 - Mendeley Data LA - English DB - MTMT ER - TY - CONF AU - N, Sushkov AU - T, Labutin AU - N, Lobus AU - Galbács, Gábor AU - Rajkó, Róbert TI - Use of non-negative matrix factorization (NMF) for an exploratory analysis of atomic and molecular spectra of zooplankton T2 - Twelfth Winter Symposium on Chemometrics : Modern Methods of Data Analysis PB - Russian Chemometrics Society C1 - [s.l.] PY - 2020 SP - 48 EP - 49 PG - 2 UR - https://m2.mtmt.hu/api/publication/31630527 ID - 31630527 LA - English DB - MTMT ER -