The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Unsupervised Learning by Probabilistic Latent Semantic Analysis
Open accessOverview
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
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
Neal, R. M., & Hinton, G. E. (1998) · Learning in Graphical Models · doi:10.1007/978-94-011-5014-9_12 · Cited by 2,187
Cited by
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Interactive machine learning for health informatics: when do we need the human-in-the-loop?
Holzinger, A. (2016) · Brain Informatics · doi:10.1007/s40708-016-0042-6 · Cited by 906
A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science
Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., & Aljaaf, A. J. (2020) · Unsupervised and Semi-Supervised Learning · doi:10.1007/978-3-030-22475-2_1 · Cited by 702
Deep learning enabled inverse design in nanophotonics
So, S., Badloe, T., Noh, J., Rho, J., & Bravo‐Abad, J. (2020) · Nanophotonics · doi:10.1515/nanoph-2019-0474 · Cited by 564
Metadata
- Authors
- Hofmann, T.
- Venue
- Machine Learning
- Published
- 2001-01
- Volume
- 42
- Issue
- 1-2
- Pages
- 177-196
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2134731454
- Cited by
- 2,456
- References
- 3
- FWCI
- 22.16
- Type
- journal article
- Language
- en
- Open access
- bronze
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.
