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

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PCA versus LDA

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

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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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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
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2001
Volume: 23
Issue: 2
Pages: 228-233
ISSN: 0162-8828 (ISSN-L)
DOI: 10.1109/34.908974
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
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
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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