The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Learning a Mahalanobis distance metric for data clustering and classification
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
Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
(2012) · Encyclopedia of the Sciences of Learning · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,370
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598228 · Cited by 11,707
Russell, E. L., Chiang, L. H., & Braatz, R. D. (2000) · Advances in Industrial Control · doi:10.1007/978-1-4471-0409-4_3 · Cited by 7,862
Distance Metric Learning for Large Margin Nearest Neighbor Classification
Weinberger, K. Q., & Saul, L. K. (2009) · Journal of Machine Learning Research · doi:10.5555/1577069.1577078 · Cited by 4,044
Learning a Similarity Metric Discriminatively, with Application to Face Verification
Chopra, S., Hadsell, R., & LeCun, Y. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.202 · Cited by 4,036
Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images
Boykov, Y. Y., & Jolly, M.-P. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937505 · Cited by 3,674
2011 International Conference on Computer Vision
(2011) · doi:10.1109/iccv17851.2011 · Cited by 2,860
Distance Metric Learning with Application to Clustering with Side-Information
Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2002) · Cited by 2,700
Parameterisation of a stochastic model for human face identification
Samaria, F. S., & Harter, A. C. (2002) · Proceedings of 1994 IEEE Workshop on Applications of Computer Vision · doi:10.1109/acv.1994.341300 · Cited by 2,593
Cited by
Unsupervised deep embedding for clustering analysis
Xie, J., Girshick, R., & Farhadi, A. (2015) · arXiv · doi:10.48550/arxiv.1511.06335 · Cited by 2,476
Reidentification by Relative Distance Comparison
Zheng, W.-S., Gong, S., & Xiang, T. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.138 · Cited by 719
Deep feature learning with relative distance comparison for person re-identification
Ding, S., Lin, L., Wang, G., & Chao, H. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.04.005 · Cited by 684
Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges
Simester, D., Timoshenko, A., & Zoumpoulis, S. I. (2020) · Management Science · doi:10.1287/mnsc.2019.3308 · Cited by 133
Digiacomo, L., Cardarelli, F., Pozzi, D., Palchetti, S., Digman, M. A., Gratton, E., Capriotti, A. L., Mahmoudi, M., & Caracciolo, G. (2017) · Nanoscale · doi:10.1039/c7nr06437c · Cited by 109
Metadata
- Authors
- Xiang, S., Nie, F., & Zhang, C.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2008-12
- Volume
- 41
- Issue
- 12
- Pages
- 3600-3612
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2062112832
- Cited by
- 621
- References
- 47
- FWCI
- 32.93
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Learning a Mahalanobis distance metric for data clustering and classification. https://app.cochat.ai/papers/10.1016/j.patcog.2008.05.018
- The paper
- Xiang, S., Nie, F., & Zhang, C. (2008). Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recognition. https://doi.org/10.1016/j.patcog.2008.05.018
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.
