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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
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 ›
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