Cited by 3,249Face and Expression RecognitionStatistical Methods and ApplicationsFace recognition and analysis

PCA versus LDA

Martinez, A.M., Kak, A.C.·2001·IEEE Transactions on Pattern Analysis and Machine Intelligence

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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Crossref · 10.1109/34.908974

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  • Feature extraction and feature selection can reduce the dimensionality of the data space.

    “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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Authors
Martinez, A. M., & Kak, A. C.
Affiliations
Purdue University West Lafayette (US)
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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