The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Multilabel Classification with Principal Label Space Transformation
Abstract
We consider a hypercube view to perceive the label space of multilabel classification problems geometrically. The view allows us not only to unify many existing multilabel classification approaches but also design a novel algorithm, principal label space transformation (PLST), that captures key correlations between labels before learning. The simple and efficient PLST relies on only singular value decomposition as the key step. We derive the theoretical guarantee of PLST and evaluate its empirical performance using real-world data sets. Experimental results demonstrate that PLST is faster than the traditional binary relevance approach and is superior to the modern compressive sensing approach in terms of both accuracy and efficiency.
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
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Advances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 8,890
Proceedings of the 25th international conference on Machine learning
Cohen, W. W., McCallum, A., & Roweis, S. T. (2008) · Cited by 5,691
ML-KNN: A lazy learning approach to multi-label learning
Zhang, M.-L., & Zhou, Z.-H. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2006.12.019 · Cited by 3,552
(2000) · CrossRef Listing of Deleted DOIs · doi:10.1162/jmlr.2003.3.4-5.993 · Cited by 3,024
Advances in Neural Information Processing Systems 20
Bonilla, E. V., Chai, K. M. A., & Williams, C. K. I. (2008) · neural information processing systems · Cited by 2,701
Learning multi-label scene classification
Boutell, M. R., Luo, J., Shen, X., & Brown, C. M. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2004.03.009 · Cited by 2,296
Cited by
Deep Learning for Extreme Multi-label Text Classification
Liu, J., Chang, W.-C., Wu, Y., & Yang, Y. (2017) · Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3077136.3080834 · Cited by 676
A Tutorial on Multilabel Learning
Gibaja, E., & Ventura, S. (2015) · ACM Computing Surveys · doi:10.1145/2716262 · Cited by 527
Metadata
- Authors
- Tai, F., & Lin, H.-T.
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 2012-09
- Volume
- 24
- Issue
- 9
- Pages
- 2508-2542
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2027869746
- Cited by
- 274
- References
- 32
- FWCI
- 15.83
- 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: Multilabel Classification with Principal Label Space Transformation. https://app.cochat.ai/papers/10.1162/neco_a_00320
- The paper
- Tai, F., & Lin, H.-T. (2012). Multilabel Classification with Principal Label Space Transformation. Neural Computation. https://doi.org/10.1162/neco_a_00320
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.
