PCA versus LDA

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...

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Bibliographic Details
Published inIEEE transactions on pattern analysis and machine intelligence Vol. 23; no. 2; pp. 228 - 233
Main Authors Martinez, A.M., Kak, A.C.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.02.2001
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Discriminant analysis
Face recognition
Intelligence
Linear discriminant analysis
Mobile robots
Object recognition
Pattern analysis
Pattern recognition
Principal component analysis
Service robots
Switches
Training
Training data
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0162-8828
1939-3539
DOI:10.1109/34.908974