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
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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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