High-dimensional data clustering
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Claims the paper makes
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
GMMs are particularly effective due to their robustness in representing high-dimensional continuous distributions.
“We propose to use GMMs as they are particularly effective due to their robustness in representing high-dimensional continuous distributions [14, 5].”
From Improving Environment Novelty Quantification for Effective Unsupervised Environment Design (2024) · cites this paper as [14]
Cites
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588 · see citing works
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A tutorial on task-parameterized movement learning and retrieval
Calinon, S. (2016) · Intelligent Service Robotics · doi:10.1007/s11370-015-0187-9 · Cited by 422 · see citing works
Beijers, L., Wardenaar, K. J., van Loo, H. M., & Schoevers, R. A. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0385-5 · Cited by 240 · see citing works
Subedi, S., & McNicholas, P. D. (2014) · Advances in Data Analysis and Classification · doi:10.1007/s11634-014-0165-7 · Cited by 51 · see citing works
Improving Environment Novelty Quantification for Effective Unsupervised Environment Design
Teoh, J., Li, W., & Varakantham, P. (2025) · arXiv
“We propose to use GMMs as they are particularly effective due to their robustness in representing high-dimensional continuous distributions [14, 5].”
Improving Environment Novelty Quantification for Effective Unsupervised Environment Design
Teoh, J., Li, W., & Varakantham, P. (2024) · Advances in Neural Information Processing Systems 37 · doi:10.52202/079017-4300
- Authors
- Bouveyron, C., Girard, S., & Schmid, C.
- Affiliations
- Centre Inria de l'Université Grenoble Alpes (FR)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Corresponding author
- Bouveyron, C.
- Venue
- Computational Statistics & Data Analysis
- Publisher
- Elsevier BV
- Published
- 2007-09
- Volume
- 52
- Issue
- 1
- Pages
- 502-519
- ISSN
- 0167-9473 (ISSN-L)
- OpenAlex
- W2095373758
- Cited by
- 382
- References
- 1
- Citations by year
- 2026: 8 · 2025: 15 · 2024: 17 · 2023: 23 · 2022: 31 · 2021: 25 · 2020: 24 · 2019: 44 · 2018: 20 · 2017: 12 · 2016: 11 · 2015: 22 · 2014: 24 · 2013: 27 · 2012: 17
- FWCI
- 14.16
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Computational Statistics & Data Analysis (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- HAL (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- high-dimensional data clustering, subspace clustering, Gaussian mixture model, parsimonious modeling, intrinsic dimension estimation, expectation-maximization algorithm, image analysis
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.
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