Selecting among three‐mode principal component models of different types and complexities: A numerical convex hull based method
Abstract
Several three‐mode principal component models can be considered for the modelling of three‐way, three‐mode data, including the Candecomp/Parafac, Tucker3, Tucker2, and Tucker1 models. The following question then may be raised: given a specific data set, which of these models should be selected, and at what complexity (i.e. with how many components)? We address this question by proposing a numerical model selection heuristic based on a convex hull. Simulation results show that this heuristic performs almost perfectly, except for Tucker3 data arrays with at least one small mode and a relatively large amount of error.
Citing Literature
Number of times cited according to CrossRef: 112
- Edoardo Saccenti, José Camacho, Multivariate Exploratory Data Analysis Using Component Models, Reference Module in Food Science, 10.1016/B978-0-08-100596-5.22902-8, (2020).
- D. Brandoni, V. Simoncini, Tensor-Train decomposition for image recognition, Calcolo, 10.1007/s10092-020-0358-8, 57, 1, (2020).
- Intensive Longitudinal Designs, The Cambridge Handbook of Research Methods in Clinical Psychology, 10.1017/9781316995808, (299-368), (2020).
- Peter C. M. Molenaar, Adriene M. Beltz, Modeling the Individual, The Cambridge Handbook of Research Methods in Clinical Psychology, 10.1017/9781316995808, (327-336), (2020).
- Giorgio Tomasi, Evrim Acar, Rasmus Bro, Multilinear Models, Iterative Methods, Reference Module in Chemistry, Molecular Sciences and Chemical Engineering, 10.1016/B978-0-12-409547-2.14609-8, (2020).
- Jose Camacho, Evrim Acar, Morten A. Rasmussen, Rasmus Bro, Cross-product penalized component analysis (X-CAN), Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2020.104038, 203, (104038), (2020).
- Rosaria Lombardo, Ida Camminatiello, Antonello D'Ambra, Eric J. Beh, Assessing the Italian tax courts system by weighted three-way log-ratio analysis, Socio-Economic Planning Sciences, 10.1016/j.seps.2020.100870, (100870), (2020).
- Sofia Fernandes, Hadi Fanaee-T, João Gama, NORMO: A new method for estimating the number of components in CP tensor decomposition, Engineering Applications of Artificial Intelligence, 10.1016/j.engappai.2020.103926, 96, (103926), (2020).
- Sopiko Gvaladze, Kim De Roover, Francis Tuerlinckx, Eva Ceulemans, Detecting outlying variables in multigroup data: A comparison of different loading similarity coefficients, Journal of Chemometrics, 10.1002/cem.3233, 0, 0, (2020).
- Jonas Zaman, Dieter Struyf, Eva Ceulemans, Bram Vervliet, Tom Beckers, Perceptual errors are related to shifts in generalization of conditioned responding, Psychological Research, 10.1007/s00426-020-01345-w, (2020).
- Véronique Cariou, Marie‐Cécile Alexandre‐Gouabau, Tom F. Wilderjans, Three‐way clustering around latent variables approach with constraints on the configurations to facilitate interpretation, Journal of Chemometrics, 10.1002/cem.3269, 0, 0, (2020).
- Keisuke Takano, Mina Stefanovic, Tabea Rosenkranz, Thomas Ehring, Clustering Individuals on Limited Features of a Vector Autoregressive Model, Multivariate Behavioral Research, 10.1080/00273171.2020.1767532, (1-19), (2020).
- Jerzy Grobelny, Rafał Michalski, Gerhard-Wilhelm Weber, Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic, Neural Computing and Applications, 10.1007/s00521-020-05363-y, (2020).
- Sofia Fernandes, Hadi Fanaee-T, João Gama, Tensor decomposition for analysing time-evolving social networks: an overview, Artificial Intelligence Review, 10.1007/s10462-020-09916-4, (2020).
- Jonas Zaman, Eva Ceulemans, Dirk Hermans, Tom Beckers, Direct and indirect effects of perception on generalization gradients, Behaviour Research and Therapy, 10.1016/j.brat.2019.01.006, (2019).
- Violetta Simonacci, Michele Gallo, Improving PARAFAC-ALS estimates with a double optimization procedure, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2019.103822, (103822), (2019).
- Zhengguo Gu, Niek C. de Schipper, Katrijn Van Deun, Variable Selection in the Regularized Simultaneous Component Analysis Method for Multi-Source Data Integration, Scientific Reports, 10.1038/s41598-019-54673-2, 9, 1, (2019).
- Carmen H Marinho, Erica Giarratano, Claudia E Domini, Mariano Garrido, Mónica N Gil, Potential mobility assessment of metals in salt marsh sediments from San Antonio Bay, Environmental Monitoring and Assessment, 10.1007/s10661-019-7895-0, 191, 12, (2019).
- S. de Vos, S. Patten, E. C. Wit, E. H. Bos, K. J. Wardenaar, P. de Jonge, Subtyping psychological distress in the population: a semi-parametric network approach, Epidemiology and Psychiatric Sciences, 10.1017/S204579601900026X, (1-8), (2019).
- Leogildo Alves Freires, Gleidson Diego Lopes Loureto, Maria Gabriela Costa Ribeiro, Layrtthon Carlos de Oliveira Santos, Valdiney Veloso Gouveia, Dispositional Greed Scale: evidências de sua estrutura interna e parâmetros dos itens, Psico-USF, 10.1590/1413-82712019240307, 24, 3, (489-500), (2019).
- Sopiko Gvaladze, Kim De Roover, Francis Tuerlinckx, Eva Ceulemans, Detecting which variables alter component interpretation across multiple groups: A resampling-based method, Behavior Research Methods, 10.3758/s13428-019-01222-4, (2019).
- Filip Calders, Patricia Bijttebier, Guy Bosmans, Eva Ceulemans, Hilde Colpin, Luc Goossens, Wim Van Den Noortgate, Karine Verschueren, Karla Van Leeuwen, Investigating the interplay between parenting dimensions and styles, and the association with adolescent outcomes, European Child & Adolescent Psychiatry, 10.1007/s00787-019-01349-x, (2019).
- Jeffrey Durieux, Tom F. Wilderjans, Partitioning subjects based on high-dimensional fMRI data: comparison of several clustering methods and studying the influence of ICA data reduction in big data, Behaviormetrika, 10.1007/s41237-019-00086-4, (2019).
- Violetta Simonacci, Michele Gallo, An ATLD–ALS method for the trilinear decomposition of large third-order tensors, Soft Computing, 10.1007/s00500-019-04320-9, (2019).
- Sofie Kuppens, Eva Ceulemans, Parenting Styles: A Closer Look at a Well-Known Concept, Journal of Child and Family Studies, 10.1007/s10826-018-1242-x, 28, 1, (168-181), (2018).
- Véronique Cariou, Tom F. Wilderjans, Consumer segmentation in multi-attribute product evaluation by means of non-negatively constrained CLV3W, Food Quality and Preference, 10.1016/j.foodqual.2017.01.006, 67, (18-26), (2018).
- Marlies Vervloet, Wim Van den Noortgate, Eva Ceulemans, Retrieving relevant factors with exploratory SEM and principal-covariate regression: A comparison, Behavior Research Methods, 10.3758/s13428-018-1022-y, 50, 4, (1430-1445), (2018).
- Marieke E. Timmerman, Urbano Lorenzo‐Seva, Eva Ceulemans, The Number of Factors Problem, The Wiley Handbook of Psychometric Testing, 10.1002/9781118489772, (305-324), (2018).
- Michele Gallo, Violetta Simonacci, Maria Anna Di Palma, An integrated algorithm for three-way compositional data, Quality & Quantity, 10.1007/s11135-018-0745-2, (2018).
- Violetta Simonacci, Michele Gallo, Detecting Public Social Spending Patterns in Italy Using a Three-Way Relative Variation Approach, Social Indicators Research, 10.1007/s11205-018-1894-3, (2018).
- Kirsten Bulteel, Francis Tuerlinckx, Annette Brose, Eva Ceulemans, Improved Insight into and Prediction of Network Dynamics by Combining VAR and Dimension Reduction, Multivariate Behavioral Research, 10.1080/00273171.2018.1516540, (1-23), (2018).
- Alwin Stegeman, Simultaneous Component Analysis by Means of Tucker3, Psychometrika, 10.1007/s11336-017-9568-7, 83, 1, (21-47), (2017).
- Paolo Giordani, Henk A.L. Kiers, A review of tensor‐based methods and their application to hospital care data, Statistics in Medicine, 10.1002/sim.7514, 37, 1, (137-156), (2017).
- M. A. Di Palma, P. Filzmoser, M. Gallo, K. Hron, A robust Parafac model for compositional data, Journal of Applied Statistics, 10.1080/02664763.2017.1381669, 45, 8, (1347-1369), (2017).
- Xiushan Nie, Yilong Yin, Jiande Sun, Ju Liu, Chaoran Cui, Comprehensive Feature-Based Robust Video Fingerprinting Using Tensor Model, IEEE Transactions on Multimedia, 10.1109/TMM.2016.2629758, 19, 4, (785-796), (2017).
- Kefei Liu, Florian Roemer, Joao Paulo C. L. da Costa, Jie Xiong, Yi-Sheng Yan, Wen-Qin Wang, Giovanni Del Galdo, undefined, 2017 25th European Signal Processing Conference (EUSIPCO), 10.23919/EUSIPCO.2017.8081287, (648-652), (2017).
- Arash Golibagh Mahyari, David M. Zoltowski, Edward M. Bernat, Selin Aviyente, A Tensor Decomposition-Based Approach for Detecting Dynamic Network States From EEG, IEEE Transactions on Biomedical Engineering, 10.1109/TBME.2016.2553960, 64, 1, (225-237), (2017).
- Anton Tenyakov, Rogemar Mamon, A computing platform for pairs-trading online implementation via a blended Kalman-HMM filtering approach, Journal of Big Data, 10.1186/s40537-017-0106-3, 4, 1, (2017).
- Jan Schepers, Hans-Hermann Bock, Iven Van Mechelen, Maximal Interaction Two-Mode Clustering, Journal of Classification, 10.1007/s00357-017-9226-x, 34, 1, (49-75), (2017).
- Lisa L. Doove, Tom F. Wilderjans, Antonio Calcagnì, Iven Van Mechelen, Deriving optimal data-analytic regimes from benchmarking studies, Computational Statistics & Data Analysis, 10.1016/j.csda.2016.10.016, 107, (81-91), (2017).
- Nadine Stammel, Estelle Bockers, Frank Neuner, Sotheara Chhim, Sopheap Taing, Christine Knaevelsrud, The Readiness to Reconcile Inventory, European Journal of Psychological Assessment, 10.1027/1015-5759/a000304, 33, 6, (436-444), (2017).
- Edoardo Saccenti, Marieke E. Timmerman, Considering Horn’s Parallel Analysis from a Random Matrix Theory Point of View, Psychometrika, 10.1007/s11336-016-9515-z, 82, 1, (186-209), (2016).
- Dorien Jansen, Katja Petry, Eva Ceulemans, Ilse Noens, Dieter Baeyens, Functioning and participation problems of students with ASD in higher education: which reasonable accommodations are effective?, European Journal of Special Needs Education, 10.1080/08856257.2016.1254962, 32, 1, (71-88), (2016).
- Susana Mendes, M. José Fernández-Gómez, Sónia Cotrim Marques, Miguel Ângelo Pardal, Ulisses Miranda Azeiteiro, M. Purificación Galindo-Villardón, CO-tucker: a new method for the simultaneous analysis of a sequence of paired tables, Journal of Applied Statistics, 10.1080/02664763.2016.1261815, 44, 15, (2729-2755), (2016).
- Tom F. Wilderjans, Véronique Cariou, CLV3W: A clustering around latent variables approach to detect panel disagreement in three-way conventional sensory profiling data, Food Quality and Preference, 10.1016/j.foodqual.2015.03.013, 47, (45-53), (2016).
- Thiago Gomes Nascimento, Carlos Eduardo Pimentel, Breno Geovanni Adaid-Castro, Escala de Atitudes frente à Arma de Fogo (EAFAF): Evidências de Sua Adequação Psicométrica, Psicologia: Teoria e Pesquisa, 10.1590/0102-3772201602187239248, 32, 1, (239-248), (2016).
- Joke Heylen, Iven Van Mechelen, Eiko I. Fried, Eva Ceulemans, Two-mode K-spectral centroid analysis for studying multivariate longitudinal profiles, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2016.03.009, 154, (194-206), (2016).
- Abir Zidi, Julien Marot, Salah Bourennane, Klaus Spinnler, undefined, 2016 IEEE International Conference on Image Processing (ICIP), 10.1109/ICIP.2016.7532930, (3101-3105), (2016).
- Anton Tenyakov, Rogemar Mamon, Matt Davison, Modelling high-frequency FX rate dynamics: A zero-delay multi-dimensional HMM-based approach, Knowledge-Based Systems, 10.1016/j.knosys.2016.03.014, 101, (142-155), (2016).
- Anton Tenyakov, Rogemar Mamon, Matt Davison, Filtering of a Discrete-Time HMM-Driven Multivariate Ornstein-Uhlenbeck Model With Application to Forecasting Market Liquidity Regimes, IEEE Journal of Selected Topics in Signal Processing, 10.1109/JSTSP.2016.2549499, 10, 6, (994-1005), (2016).
- Hadi Fanaee-T, João Gama, Tensor-based anomaly detection: An interdisciplinary survey, Knowledge-Based Systems, 10.1016/j.knosys.2016.01.027, 98, (130-147), (2016).
- Saeed Pouryazdian, Soosan Beheshti, Sridhar Krishnan, CANDECOMP/PARAFAC model order selection based on Reconstruction Error in the presence of Kronecker structured colored noise, Digital Signal Processing, 10.1016/j.dsp.2015.08.014, 48, (12-26), (2016).
- Huachun Tan, Yuankai Wu, Bin Shen, Peter J. Jin, Bin Ran, Short-Term Traffic Prediction Based on Dynamic Tensor Completion, IEEE Transactions on Intelligent Transportation Systems, 10.1109/TITS.2015.2513411, 17, 8, (2123-2133), (2016).
- Kefei Liu, João Paulo C.L. da Costa, Hing Cheung So, Lei Huang, Jieping Ye, Detection of number of components in CANDECOMP/PARAFAC models via minimum description length, Digital Signal Processing, 10.1016/j.dsp.2016.01.003, 51, (110-123), (2016).
- Kim De Roover, Eva Ceulemans, Paolo Giordani, Overlapping Clusterwise Simultaneous Component Analysis, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2016.05.002, 156, (249-259), (2016).
- Marieke E. Timmerman, Henk A.L. Kiers, Eva Ceulemans, Searching components with simple structure in simultaneous component analysis: Blockwise Simplimax rotation, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2016.05.001, 156, (260-272), (2016).
- Ioannis Delis, Arno Onken, Philippe G. Schyns, Stefano Panzeri, Marios G. Philiastides, Space-by-time decomposition for single-trial decoding of M/EEG activity, NeuroImage, 10.1016/j.neuroimage.2016.03.043, 133, (504-515), (2016).
- Kirsten Bulteel, Francis Tuerlinckx, Annette Brose, Eva Ceulemans, Clustering Vector Autoregressive Models: Capturing Qualitative Differences in Within-Person Dynamics, Frontiers in Psychology, 10.3389/fpsyg.2016.01540, 7, (2016).
- Kim De Roover, Marieke E. Timmerman, Eva Ceulemans, How to detect which variables are causing differences in component structure among different groups, Behavior Research Methods, 10.3758/s13428-015-0687-8, 49, 1, (216-229), (2015).
- Cristina Tortora, Mireille Gettler Summa, Marina Marino, Francesco Palumbo, Factor probabilistic distance clustering (FPDC): a new clustering method, Advances in Data Analysis and Classification, 10.1007/s11634-015-0219-5, 10, 4, (441-464), (2015).
- Eva Ceulemans, Tom F. Wilderjans, Henk A. L. Kiers, Marieke E. Timmerman, MultiLevel simultaneous component analysis: A computational shortcut and software package, Behavior Research Methods, 10.3758/s13428-015-0626-8, 48, 3, (1008-1020), (2015).
- Andrzej Cichocki, Danilo Mandic, Lieven De Lathauwer, Guoxu Zhou, Qibin Zhao, Cesar Caiafa, HUY ANH PHAN, Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis, IEEE Signal Processing Magazine, 10.1109/MSP.2013.2297439, 32, 2, (145-163), (2015).
- Edoardo Saccenti, José Camacho, Determining the number of components in principal components analysis: A comparison of statistical, crossvalidation and approximated methods, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2015.10.006, 149, (99-116), (2015).
- Marieke E. Timmerman, Huub C. J. Hoefsloot, Age K. Smilde, Eva Ceulemans, Scaling in ANOVA-simultaneous component analysis, Metabolomics, 10.1007/s11306-015-0785-8, 11, 5, (1265-1276), (2015).
- Yuko Nakamura, Nonmetric three-mode principal component analysis for qualitative data, Kodo Keiryogaku (The Japanese Journal of Behaviormetrics), 10.2333/jbhmk.42.105, 42, 2, (105-115), (2015).
- Chiheb-Eddine Ben N’Cir, Nadia Essoussi, Mohamed Limam, Kernel-Based Methods to Identify Overlapping Clusters with Linear and Nonlinear Boundaries, Journal of Classification, 10.1007/s00357-015-9181-3, 32, 2, (176-211), (2015).
- Jean‐Philip Royer, Nadège Thirion‐Moreau, Pierre Comon, Roland Redon, Stéphane Mounier, A regularized nonnegative canonical polyadic decomposition algorithm with preprocessing for 3D fluorescence spectroscopy, Journal of Chemometrics, 10.1002/cem.2709, 29, 4, (253-265), (2015).
- Bilian Chen, Zhening Li, Shuzhong Zhang, On optimal low rank Tucker approximation for tensors: the case for an adjustable core size, Journal of Global Optimization, 10.1007/s10898-014-0231-x, 62, 4, (811-832), (2014).
- Kirsten Bulteel, Eva Ceulemans, Renee J. Thompson, Christian E. Waugh, Ian H. Gotlib, Francis Tuerlinckx, Peter Kuppens, DeCon: A tool to detect emotional concordance in multivariate time series data of emotional responding, Biological Psychology, 10.1016/j.biopsycho.2013.10.011, 98, (29-42), (2014).
- Suhas Tikole, Victor Jaravine, Vladimir Rogov, Volker Dötsch, Peter Güntert, Peak picking NMR spectral data using non-negative matrix factorization, BMC Bioinformatics, 10.1186/1471-2105-15-46, 15, 1, (46), (2014).
- Alwin Stegeman, Finding the limit of diverging components in three-way Candecomp/Parafac—A demonstration of its practical merits, Computational Statistics & Data Analysis, 10.1016/j.csda.2014.02.010, 75, (203-216), (2014).
- Mónica B. Alvarez, Pamela Y. Quintas, Claudia E. Domini, Mariano Garrido, Beatriz S. Fernández Band, Chemometric approach to visualize and easily interpret data from sequential extraction procedures applied to sediment samples, Journal of Hazardous Materials, 10.1016/j.jhazmat.2014.04.039, 274, (455-464), (2014).
- Jinhong Zhong, Ke Tang, A. K. Qin, undefined, 2014 International Joint Conference on Neural Networks (IJCNN), 10.1109/IJCNN.2014.6889699, (1587-1592), (2014).
- Kim De Roover, Marieke E. Timmerman, Jozefien De Leersnyder, Batja Mesquita, Eva Ceulemans, What's hampering measurement invariance: detecting non-invariant items using clusterwise simultaneous component analysis, Frontiers in Psychology, 10.3389/fpsyg.2014.00604, 5, (2014).
- Alwin Stegeman, Tam T. T. Lam, Three-Mode Factor Analysis by Means of Candecomp/Parafac, Psychometrika, 10.1007/s11336-013-9359-8, 79, 3, (426-443), (2013).
- Austin J. Brockmeier, Jose C. Principe, Anh Huy Phan, Andrzej Cichocki, undefined, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 10.1109/ICASSP.2013.6638839, (6113-6117), (2013).
- Tom F. Wilderjans, Eva Ceulemans, Clusterwise Parafac to identify heterogeneity in three-way data, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2013.09.010, 129, (87-97), (2013).
- Kim De Roover, Eva Ceulemans, Marieke E. Timmerman, John B. Nezlek, Patrick Onghena, Modeling Differences in the Dimensionality of Multiblock Data by Means of Clusterwise Simultaneous Component Analysis, Psychometrika, 10.1007/s11336-013-9318-4, 78, 4, (648-668), (2013).
- Kirsten Bulteel, Tom F. Wilderjans, Francis Tuerlinckx, Eva Ceulemans, CHull as an alternative to AIC and BIC in the context of mixtures of factor analyzers, Behavior Research Methods, 10.3758/s13428-012-0293-y, 45, 3, (782-791), (2013).
- Marieke E. Timmerman, Eva Ceulemans, Kim De Roover, Karla Van Leeuwen, Subspace K-means clustering, Behavior Research Methods, 10.3758/s13428-013-0329-y, 45, 4, (1011-1023), (2013).
- Kim De Roover, Marieke E. Timmerman, Iven Van Mechelen, Eva Ceulemans, On the added value of multiset methods for three-way data analysis, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2013.05.002, 129, (98-107), (2013).
- Fabian Jasper, Urs M. Nater, Wolfgang Hiller, Ulrike Ehlert, Susanne Fischer, Michael Witthöft, Rasch scalability of the somatosensory amplification scale: A mixture distribution approach, Journal of Psychosomatic Research, 10.1016/j.jpsychores.2013.02.006, 74, 6, (469-478), (2013).
- Tom F. Wilderjans, Dirk Depril, Iven Van Mechelen, Additive Biclustering: A Comparison of One New and Two Existing ALS Algorithms, Journal of Classification, 10.1007/s00357-013-9120-0, 30, 1, (56-74), (2013).
- Urbano Lorenzo-Seva, Fabia Morales-Vives, Andreu Vigil-Colet, Aggressive Responses to Troubled Situations in a Sample of Adolescents: A Three-Mode Approach, The Spanish journal of psychology, 10.1017/S1138741600003760, 13, 1, (178-189), (2013).
- Tom F. Wilderjans, Eva Ceulemans, Kristof Meers, CHull: A generic convex-hull-based model selection method, Behavior Research Methods, 10.3758/s13428-012-0238-5, 45, 1, (1-15), (2012).
- Kim De Roover, Eva Ceulemans, Marieke E. Timmerman, Patrick Onghena, A clusterwise simultaneous component method for capturing within‐cluster differences in component variances and correlations, British Journal of Mathematical and Statistical Psychology, 10.1111/j.2044-8317.2012.02040.x, 66, 1, (81-102), (2012).
- Dirk Depril, Iven Van Mechelen, Tom F. Wilderjans, Lowdimensional Additive Overlapping Clustering, Journal of Classification, 10.1007/s00357-012-9112-5, 29, 3, (297-320), (2012).
- Kim De Roover, Eva Ceulemans, Marieke E. Timmerman, How to perform multiblock component analysis in practice, Behavior Research Methods, 10.3758/s13428-011-0129-1, 44, 1, (41-56), (2011).
- Eva Ceulemans, Peter Kuppens, Iven Van Mechelen, Capturing the Structure of Distinct Types of Individual Differences in the Situation‐specific Experience of Emotions: The Case of Anger, European Journal of Personality, 10.1002/per.847, 26, 5, (484-495), (2011).
- Urbano Lorenzo‐Seva, Pere J. Ferrando, A procedure for isolating social desirability variance in a three‐way component analysis, British Journal of Mathematical and Statistical Psychology, 10.1111/j.2044-8317.2011.02015.x, 65, 1, (74-88), (2011).
- Kohei Adachi, Three-Way Tucker2 Component Analysis Solutions of Stimuli × Responses × Individuals Data with Simple Structure and the Fewest Core Differences, Psychometrika, 10.1007/s11336-011-9208-6, 76, 2, (285-305), (2011).
- Urbano Lorenzo-Seva, Marieke E. Timmerman, Henk A. L. Kiers, The Hull Method for Selecting the Number of Common Factors, Multivariate Behavioral Research, 10.1080/00273171.2011.564527, 46, 2, (340-364), (2011).
- Eva Ceulemans, Marieke E. Timmerman, Henk A.L. Kiers, The CHull procedure for selecting among multilevel component solutions, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2010.08.001, 106, 1, (12-20), (2011).
- Jan Schepers, Iven Van Mechelen, Eva Ceulemans, The Real-Valued Model of Hierarchical Classes, Journal of Classification, 10.1007/s00357-011-9089-5, 28, 3, (363-389), (2011).
- Caterina Durante, Rasmus Bro, Marina Cocchi, A classification tool for N-way array based on SIMCA methodology, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2010.09.004, 106, 1, (73-85), (2011).
- Eva Ceulemans, Henk A. L. Kiers, Discriminating between strong and weak structures in three‐mode principal component analysis, British Journal of Mathematical and Statistical Psychology, 10.1348/000711008X369474, 62, 3, (601-620), (2011).
- Eva Ceulemans, Gert Storms, Detecting intra- and inter-categorical structure in semantic concepts using HICLAS, Acta Psychologica, 10.1016/j.actpsy.2009.11.011, 133, 3, (296-304), (2010).
- undefined Zhaoshui He, Andrzej Cichocki, undefined Shengli Xie, undefined Kyuwan Choi, Detecting the Number of Clusters in n-Way Probabilistic Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 10.1109/TPAMI.2010.15, 32, 11, (2006-2021), (2010).
- Panagiotis Symeonidis, Alexandros Nanopoulos, Yannis Manolopoulos, A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis, IEEE Transactions on Knowledge and Data Engineering, 10.1109/TKDE.2009.85, 22, 2, (179-192), (2010).
- Marieke E. Timmerman, Eva Ceulemans, Henk A.L. Kiers, Maurizio Vichi, Factorial and reduced K-means reconsidered, Computational Statistics & Data Analysis, 10.1016/j.csda.2010.02.009, 54, 7, (1858-1871), (2010).
- See more




