The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
PCA versus LDA
Abstract
In the context of the appearance-based paradigm for object recognition, it is generally believed that algorithms based on LDA (linear discriminant analysis) are superior to those based on PCA (principal components analysis). In this communication, we show that this is not always the case. We present our case first by using intuitively plausible arguments and, then, by showing actual results on a face database. Our overall conclusion is that when the training data set is small, PCA can outperform LDA and, also, that PCA is less sensitive to different training data sets.
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“The dimensional space of the data can be reduced by employing dimensionality reduction techniques such as feature extraction and feature selection.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Martinez and Kak, 2001
Citations
Cites
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Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
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The FERET evaluation methodology for face-recognition algorithms
Phillips, P. J., Moon, H., Rizvi, S. A., & Rauss, P. J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.879790 · Cited by 3,692
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Swets, D. L., & Weng, J. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.531802 · Cited by 1,627
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Moghaddam, B., & Pentland, A. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598227 · Cited by 1,444
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THE STATISTICAL UTILIZATION OF MULTIPLE MEASUREMENTS
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The FERET evaluation methodology for face-recognition algorithms
Phillips, P. J., Moon, H., Rauss, P. J., & Rizvi, S. A. (2002) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1997.609311 · Cited by 589
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Flagged first, then by citations
Research works printed but not linked: 4
- Moon, 1998, Analysis of PCA-based Face Recognition Algorithms
- Murase, 1981, An Improvement of the Autocorrelation Matrix in Pattern Matching Method and Its Application to Handprinted ’HIRAGANA’, Trans. IECE Japan, 64D
- Pentland, Experiments with Eigenfaces
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Cited by
Sorted by citations
Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003) · ACM Computing Surveys · doi:10.1145/954339.954342 · Cited by 6,174
Detecting faces in images: a survey
Yang, M.-H., Kriegman, D. J., & Ahuja, N. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982883 · Cited by 3,458
Face recognition using Laplacianfaces
He, X., Yan, S., Hu, Y., Niyogi, P., & Zhang, H.-J. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.55 · Cited by 3,268
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
Yan, S., Xu, D., Zhang, B., Zhang, H.-J., Yang, Q., & Lin, S. C.-F. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.250598 · Cited by 3,035
Histopathological Image Analysis: A Review
Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., & Yener, B. (2009) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2009.2034865 · Cited by 2,052
Metadata
- Authors
- Martinez, A. M., & Kak, A. C.
- Affiliations
- Purdue University West Lafayette (US)
- ORCID
- Kak, A.C.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2001
- Volume
- 23
- Issue
- 2
- Pages
- 228-233
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2134262590
- Cited by
- 3,249
- References
- 18
- Citations by year
- 2026: 46 · 2025: 87 · 2024: 107 · 2023: 103 · 2022: 147 · 2021: 148 · 2020: 154 · 2019: 171 · 2018: 172 · 2017: 193 · 2016: 166 · 2015: 186 · 2014: 217 · 2013: 185 · 2012: 171
- FWCI
- 51.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- principal component analysis, linear discriminant analysis, appearance-based object recognition, face recognition, limited training data, dimensionality reduction
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