The DOI matches its Crossref record.
Analysis of Individual Differences in Multidimensional Scaling Via an N-way Generalization of “Eckart-Young” Decomposition
Abstract
An individual differences model for multidimensional scaling is outlined in which individuals are assumed differentially to weight the several dimensions of a common “psychological space”. A corresponding method of analyzing similarities data is proposed, involving a generalization of “Eckart-Young analysis” to decomposition of three-way (or higher-way) tables. In the present case this decomposition is applied to a derived three-way table of scalar products between stimuli for individuals. This analysis yields a stimulus by dimensions coordinate matrix and a subjects by dimensions matrix of weights. This method is illustrated with data on auditory stimuli and on perception of nations.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Generalized procrustes analysis
Gower, J. C. (1975) · Psychometrika · doi:10.1007/bf02291478 · Cited by 3,267
The structure and dynamics of multilayer networks
Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., & Zanin, M. (2014) · Physics Reports · doi:10.1016/j.physrep.2014.07.001 · Cited by 3,125
Searching for intellectual turning points: progressive knowledge domain visualization
Chen, C. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307513100 · Cited by 2,791
Knowledge Graph Embedding: A Survey of Approaches and Applications
Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2754499 · Cited by 2,654
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects
Lahat, D., Adali, T., & Jutten, C. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2460697 · Cited by 1,296
Payne, D. L., Lonsway, K. A., & Fitzgerald, L. F. (1999) · Journal of Research in Personality · doi:10.1006/jrpe.1998.2238 · Cited by 1,114
Point of view in personal memories
Nigro, G., & Neisser, U. (1983) · Cognitive Psychology · doi:10.1016/0010-0285(83)90016-6 · Cited by 827
Evidence of convergent validity on the dimensions of affect
Russell, J. A. (1978) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.36.10.1152 · Cited by 495
Tone perception in Far Eastern languages
Gandour, J. (1983) · Journal of Phonetics · doi:10.1016/s0095-4470(19)30813-7 · Cited by 473
Inductive judgments about natural categories
Rips, L. J. (1975) · Journal of Verbal Learning and Verbal Behavior · doi:10.1016/s0022-5371(75)80055-7 · Cited by 465
Interoceptive sensitivity and self-reports of emotional experience
Barrett, L. F., Quigley, K. S., Bliss-Moreau, E., & Aronson, K. R. (2004) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.87.5.684 · Cited by 432
Kim, J., He, Y., & Park, H. (2014) · Journal of Global Optimization · doi:10.1007/s10898-013-0035-4 · Cited by 352
Feeling small: exploring the tactile perception limits
Skedung, L., Arvidsson, M., Chung, J. Y., Stafford, C. M., Berglund, B., & Rutland, M. W. (2013) · Scientific Reports · doi:10.1038/srep02617 · Cited by 285
Functional disconnectivity of the medial temporal lobe in Asperger's syndrome
Welchew, D. E., Ashwin, C., Berkouk, K., Salvador, R., Suckling, J., Baron-Cohen, S., & Bullmore, E. (2005) · Biological Psychiatry · doi:10.1016/j.biopsych.2005.01.028 · Cited by 207
Bolton, P., Li, T., Ravina, E., & Rosenthal, H. (2020) · Journal of Financial Economics · doi:10.1016/j.jfineco.2020.03.004 · Cited by 183
Approximation of $2^d\times2^d$ Matrices Using Tensor Decomposition
Oseledets, I. V. (2010) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/090757861 · Cited by 174
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
Xiong, Y., Liu, H., Gupta, S., Akin, B., Bender, G., Wang, Y., Kindermans, P.-J., Tan, M., Singh, V., & Chen, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00382 · Cited by 193
Network Position and Cognition in a Computer Software Firm
Walker, G. (1985) · Administrative Science Quarterly · doi:10.2307/2392814 · Cited by 129
Federated Tensor Factorization for Computational Phenotyping
Kim, Y., Sun, J., Yu, H., & Jiang, X. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098118 · Cited by 128
A Review of Integrative Imputation for Multi-Omics Datasets
Song, M., Greenbaum, J., Luttrell, J., Zhou, W., Wu, C., Shen, H., Gong, P., Zhang, C., & Deng, H.-W. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.570255 · Cited by 127
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization
Liang, P. P., Liu, Z., Tsai, Y.-H. H., Zhao, Q., Salakhutdinov, R., & Morency, L.-P. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1152 · Cited by 95
(2019) · neural information processing systems · Cited by 83
On Multi-Relational Link Prediction with Bilinear Models
Wang, Y., Gemulla, R., & Li, H. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11738 · Cited by 65
In vivo ephaptic coupling allows memory network formation
Pinotsis, D. A., & Miller, E. K. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad251 · Cited by 62
POTTER, T., & CORNEILLE, O. (2008) · Psychonomic Bulletin & Review · doi:10.3758/pbr.15.3.615 · Cited by 58
Acar, E., Plopper, G. E., & Yener, B. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032227 · Cited by 35
Beyond dimension reduction: stable electric fields emerge from and allow representational drift
Pinotsis, D. A., & Miller, E. K. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119058 · Cited by 37
Ripoll-Zarraga, A. E., Portillo, F., & Mar-Molinero, C. (2023) · Research in Transportation Business & Management · doi:10.1016/j.rtbm.2021.100689 · Cited by 10
Using the PARAFAC2 tensor factorization on EHR audit data to understand PCP desktop work
Perros, I., Yan, X., Jones, J. B., Sun, J., & Stewart, W. F. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103312 · Cited by 8
A Tensor‐based Regression Approach for Human Motion Prediction
Gril, L., Wedenig, P., Torkar, C., & Kleb, U. (2023) · Quality and Reliability Engineering International · doi:10.1002/qre.3153 · Cited by 7
Low-Rank+Sparse Tensor Compression for Neural Networks
Hawkins, C., Yang, H., Li, M., Lai, L., & Chandra, V. (2021) · arXiv · doi:10.48550/arxiv.2111.01697 · Cited by 2
Metadata
- Authors
- Carroll, J. D., & Chang, J.-J.
- Venue
- Psychometrika
- Publisher
- Cambridge University Press (CUP)
- Published
- 1970-09
- Volume
- 35
- Issue
- 3
- Pages
- 283-319
- ISSN
- 0033-3123 (ISSN-L)
- 1860-0980
- OpenAlex
- W2000215628
- Cited by
- 4,678
- References
- 0
- FWCI
- 34.03
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.