  This paper proposes a new subspace learning method, named Quantized Fisher
Discriminant Analysis (QFDA), which makes use of both machine learning and
information theory. There is a lack of literature for combination of machine
learning and information theory and this paper tries to tackle this gap. QFDA
finds a subspace which discriminates the uniformly quantized images in the
Discrete Cosine Transform (DCT) domain at least as well as discrimination of
non-quantized images by Fisher Discriminant Analysis (FDA) while the images
have been compressed. This helps the user to throw away the original images and
keep the compressed images instead without noticeable loss of classification
accuracy. We propose a cost function whose minimization can be interpreted as
rate-distortion optimization in information theory. We also propose quantized
Fisherfaces for facial analysis in QFDA. Our experiments on AT&T face dataset
and Fashion MNIST dataset show the effectiveness of this subspace learning
method.
