In the context of the appearance-based paradigm for object recognition, it
is generally believed that algorithms based on LDA (Linear Discriminant Ana
lysis) 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 show
ing 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.