Models for asymmetric proximities
Abstract
All major models for asymmetric proximities are a combination of a symmetric similarity component and an asymmetric dominance component. The differences and similarities between the methods that are discussed in this paper are revealed by applying a certain decomposition to the model parameters, clearly separating the dominance and symmetric similarity component. The notion of skew‐symmetry turns out to be an often seen element in modelling asymmetry, although sometimes in disguise and difficult to recognize. The decomposition shows that there are two classes of models, one that assumes that the asymmetric relationships are transitive, while the other class consists of models that can also represent circular asymmetric relationships.
Citing Literature
Number of times cited according to CrossRef: 48
- Tadashi Imaizumi, On Detection of the Unique Dimensions of Asymmetry in Proximity Data, Advanced Studies in Behaviormetrics and Data Science, 10.1007/978-981-15-2700-5_7, (119-130), (2020).
- Donatella Vicari, Modeling Asymmetric Exchanges Between Clusters, Advanced Studies in Behaviormetrics and Data Science, 10.1007/978-981-15-2700-5_18, (297-313), (2020).
- Giuseppe Bove, Akinori Okada, Methods for the analysis of asymmetric pairwise relationships, Advances in Data Analysis and Classification, 10.1007/s11634-017-0307-9, 12, 1, (5-31), (2018).
- Kensuke Tanioka, Hiroshi Yadohisa, Unfolding Models for Asymmetric Dissimilarity Data With External Information Based on Path Structures, International Journal of Software Innovation, 10.4018/IJSI.2018070104, 6, 3, (53-66), (2018).
- Tadashi Imaizumi, Multi-Dimensional Scaling of Sparse Block Diagonal Similarity Matrix, Data Science, 10.1007/978-3-319-55723-6_20, (259-272), (2017).
- Kensuke Tanioka, Hiroshi Yadohisa, undefined, 2017 5th Intl Conf on Applied Computing and Information Technology/4th Intl Conf on Computational Science/Intelligence and Applied Informatics/2nd Intl Conf on Big Data, Cloud Computing, Data Science (ACIT-CSII-BCD), 10.1109/ACIT-CSII-BCD.2017.13, (320-325), (2017).
- Donatella Vicari, CLUSKEXT: CLUstering model for SKew-symmetric data including EXTernal information, Advances in Data Analysis and Classification, 10.1007/s11634-015-0203-0, 12, 1, (43-64), (2015).
- Dominik Olszewski, Asymmetric $$k$$ k -Means Clustering of the Asymmetric Self-Organizing Map, Neural Processing Letters, 10.1007/s11063-015-9415-8, 43, 1, (231-253), (2015).
- Michael J. Brusco, Patrick Doreian, Douglas Steinley, Biclustering methods for one-mode asymmetric matrices, Behavior Research Methods, 10.3758/s13428-015-0587-y, 48, 2, (487-502), (2015).
- J. Fernando Vera, Chiristian D. Rivera, A Structural Equation Multidimensional Scaling Model for One-Mode Asymmetric Dissimilarity Data, Structural Equation Modeling: A Multidisciplinary Journal, 10.1080/10705511.2014.856696, 21, 1, (54-62), (2014).
- Heeyoul Choi, Data visualization for asymmetric relations, Neurocomputing, 10.1016/j.neucom.2013.07.030, 124, (97-104), (2014).
- Dominik Olszewski, Branko Šter, Asymmetric clustering using the alpha–beta divergence, Pattern Recognition, 10.1016/j.patcog.2013.11.019, 47, 5, (2031-2041), (2014).
- Timothy C. Havens, James C. Bezdek, Christopher Leckie, Marimuthu Palaniswami, undefined, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 10.1109/FUZZ-IEEE.2013.6622300, (1-6), (2013).
- Giuseppe Bove, Asymmetric Multidimensional Scaling Models for Seriation, Statistical Models for Data Analysis, 10.1007/978-3-319-00032-9_7, (55-62), (2013).
- Timothy C. Havens, Derek T. Anderson, Christian Wagner, Hanieh Deilamsalehy, Dereck Wonnacott, undefined, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 10.1109/FUZZ-IEEE.2013.6622343, (1-8), (2013).
- Akinori Okada, Hiroyuki Tsurumi, ASYMMETRIC MULTIDIMENSIONAL SCALING OF BRAND SWITCHING AMONG MARGARINE BRANDS, Behaviormetrika, 10.2333/bhmk.39.111, 39, 1, (111-126), (2012).
- Naohito Chino, A BRIEF SURVEY OF ASYMMETRIC MDS AND SOME OPEN PROBLEMS, Behaviormetrika, 10.2333/bhmk.39.127, 39, 1, (127-165), (2012).
- Dominik Olszewski, k-Means Clustering of Asymmetric Data, Hybrid Artificial Intelligent Systems, 10.1007/978-3-642-28942-2_22, (243-254), (2012).
- Dominik Olszewski, An Experimental Study on Asymmetric Self-Organizing Map, Intelligent Data Engineering and Automated Learning - IDEAL 2011, 10.1007/978-3-642-23878-9_6, (42-49), (2011).
- I-C. Lerman, P. Kuntz, Directed Binary Hierarchies and Directed Ultrametrics, Journal of Classification, 10.1007/s00357-011-9091-y, 28, 3, (272-296), (2011).
- Akinori Okada, Towao Sakaehara, Analysis of Guarantor and Warrantee Relationships Among Government Officials in the Eighth Century in the Old Capital of Japan by Using Asymmetric Multidimensional Scaling, Advances in Data Analysis, Data Handling and Business Intelligence, 10.1007/978-3-642-01044-6_56, (605-614), (2010).
- Giuseppe Bove, Models for Asymmetry in Proximity Data, Data Analysis and Classification, 10.1007/978-3-642-03739-9_9, (79-84), (2010).
- Giuseppe Bove, Methods for the Analysis of Skew-Symmetry in Asymmetric Multidimensional Scaling, Classification as a Tool for Research, 10.1007/978-3-642-10745-0_29, (271-278), (2010).
- Michael J. Brusco, Stephanie Stahl, Bicriterion seriation methods for skew‐symmetric matrices, British Journal of Mathematical and Statistical Psychology, 10.1348/000711005X63908, 58, 2, (333-343), (2010).
- Isaac Martín de Diego, Alberto Muñoz, Javier M. Moguerza, Methods for the combination of kernel matrices within a support vector framework, Machine Learning, 10.1007/s10994-009-5135-5, 78, 1-2, (137-174), (2009).
- S. Saburi, N. Chino, A maximum likelihood method for an asymmetric MDS model, Computational Statistics & Data Analysis, 10.1016/j.csda.2008.03.011, 52, 10, (4673-4684), (2008).
- Kohei Adachi, TREND VECTOR REPRESENTATION OF MULTIPLE TRANSITION MATRICES BY PENALIZED OPTIMAL SCALING, Journal of the Japanese Society of Computational Statistics, 10.5183/jjscs1988.20.19, 20, 1, (19-37), (2007).
- Akinori Okada, Tadashi Imaizumi, Multidimensional Scaling of Asymmetric Proximities with a Dominance Point, Advances in Data Analysis, 10.1007/978-3-540-70981-7_35, (307-318), (2007).
- Wayne S. DeSarbo, Rajdeep Grewal, An alternative efficient representation of demand‐based competitive asymmetry, Strategic Management Journal, 10.1002/smj.601, 28, 7, (755-766), (2007).
- Anita Prinzie, Dirk Van den Poel, Incorporating sequential information into traditional classification models by using an element/position-sensitive SAM, Decision Support Systems, 10.1016/j.dss.2005.02.004, 42, 2, (508-526), (2006).
- Antonio Solanas, Lluís Salafranca, Carles Riba, Vicenta Sierra, David Leiva, Quantifying social asymmetric structures, Behavior Research Methods, 10.3758/BF03192792, 38, 3, (390-399), (2006).
- Giuseppe Bove, Approaches to Asymmetric Multidimensional Scaling with External Information, Data Analysis, Classification and the Forward Search, 10.1007/3-540-35978-8, (69-76), (2006).
- Michael J. Brusco, Douglas Steinley, Clustering, seriation, and subset extraction of confusion data., Psychological Methods, 10.1037/1082-989X.11.3.271, 11, 3, (271-286), (2006).
- Manuel Martín-Merino, Alberto Muñoz, Visualizing asymmetric proximities with SOM and MDS models, Neurocomputing, 10.1016/j.neucom.2004.04.010, 63, (171-192), (2005).
- Akinori Okada, Tadashi Imaizumi, Hiroshi Inoue, Asymmetric Multidimensional Scaling of Relationships Among Managers of a Firm, Data Analysis and Decision Support, 10.1007/3-540-28397-8, (100-107), (2005).
- Manuel Martín-Merino, Alberto Muñoz, Extending the SOM Algorithm to Visualize Word Relationships, Advances in Intelligent Data Analysis VI, 10.1007/11552253_21, (228-238), (2005).
- Alberto Muñoz, Isaac Martín de Diego, Javier M. Moguerza, Support Vector Machine Classifiers for Asymmetric Proximities, Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003, 10.1007/3-540-44989-2_27, (217-224), (2003).
- Mark de Rooij, Willem J. Heiser, A Distance Representation of the Quasi-Symmetry Model and Related Distance Models, New Developments in Psychometrics, 10.1007/978-4-431-66996-8, (487-494), (2003).
- Michael J. Brusco, Stephanie Stahl, Compact integer-programming models for extracting subsets of stimuli from confusion matrices, Psychometrika, 10.1007/BF02294442, 66, 3, (405-419), (2001).
- M. Martin-Merino, A. Munoz, Y. Dimitriadis, undefined, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222), 10.1109/IJCNN.2001.938455, (1908-1913), (2001).
- Michael J. Brusco, Stephanie Stahl, An interactive multiobjective programming approach to combinatorial data analysis, Psychometrika, 10.1007/BF02295729, 66, 1, (5-24), (2001).
- Manuel Martin-Merino, Alberto Muñoz, Self Organizing Map and Sammon Mapping for Asymmetric Proximities, Artificial Neural Networks — ICANN 2001, 10.1007/3-540-44668-0_60, (429-435), (2001).
- El Mostafa Qannari, Philippe Courcoux, Michel Séménou, New models for the analysis of paired comparison data; segmentation of the panel, Food Quality and Preference, 10.1016/S0950-3293(99)00056-7, 11, 1-2, (71-76), (2000).
- Akinori Okada, An Asymmetric Cluster Analysis Study of Car Switching Data, Data Analysis, 10.1007/978-3-642-58250-9_41, (495-504), (2000).
- Giuseppe Bove, Roberto Rocci, Methods for Asymmetric Three-way Scaling, Classification and Data Analysis, 10.1007/978-3-642-60126-2_17, (131-138), (1999).
- Akinori Okada, Effects of End-Aisle Display and Flier on the Brand-Switching of Instant Coffee, Data Science, Classification, and Related Methods, 10.1007/978-4-431-65950-1_78, (716-727), (1998).
- Akinori Okada, Tadashi Imaizumi, Asymmetric multidimensional scaling of two-mode three-way proximities, Journal of Classification, 10.1007/s003579900010, 14, 2, (195-224), (1997).
- Kohei Adachi, Homogeneity Analysis of Transition Matrices for Spatially Representing a Transition Trend, Behaviormetrika, 10.2333/bhmk.24.159, 24, 2, (159-178), (1997).




