The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Non-Negative Patch Alignment Framework
Abstract
In this paper, we present a non-negative patch alignment framework (NPAF) to unify popular non-negative matrix factorization (NMF) related dimension reduction algorithms. It offers a new viewpoint to better understand the common property of different NMF algorithms. Although multiplicative update rule (MUR) can solve NPAF and is easy to implement, it converges slowly. Thus, we propose a fast gradient descent (FGD) to overcome the aforementioned problem. FGD uses the Newton method to search the optimal step size, and thus converges faster than MUR. Experiments on synthetic and real-world datasets confirm the efficiency of FGD compared with MUR for optimizing NPAF. Based on NPAF, we develop non-negative discriminative locality alignment (NDLA). Experiments on face image and handwritten datasets suggest the effectiveness of NDLA in classification tasks and its robustness to image occlusions, compared with representative NMF-related dimension reduction algorithms.
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
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Cited by
Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression
Zhang, K., Gao, X., Tao, D., & Li, X. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2208977 · Cited by 576
Self-citationNeNMF: An Optimal Gradient Method for Nonnegative Matrix Factorization
Guan, N., Tao, D., Luo, Z., & Yuan, B. (2012) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2012.2190406 · Cited by 468
Self-citationOnline Nonnegative Matrix Factorization With Robust Stochastic Approximation
Guan, N., Tao, D., Luo, Z., & Yuan, B. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2197827 · Cited by 287
Self-citationEnsemble manifold regularized sparse low-rank approximation for multiview feature embedding
Zhang, L., Zhang, Q., Zhang, L., Tao, D., Huang, X., & Du, B. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2014.12.016 · Cited by 278
Self-citation
Metadata
- Authors
- Guan, N., Tao, D., Luo, Z., & Yuan, B.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2011-08
- Volume
- 22
- Issue
- 8
- Pages
- 1218-1230
- ISSN
- 1045-9227 (ISSN-L)
- 1941-0093
- OpenAlex
- W2097872774
- Cited by
- 181
- References
- 2
- FWCI
- 42.44
- 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: Non-Negative Patch Alignment Framework. https://app.cochat.ai/papers/10.1109/tnn.2011.2157359
- The paper
- Guan, N., Tao, D., Luo, Z., & Yuan, B. (2011). Non-Negative Patch Alignment Framework. IEEE Transactions on Neural Networks. https://doi.org/10.1109/tnn.2011.2157359
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.
