  Dimension reduction and data quantization are two important methods for
reducing data complexity. In the paper, we study the methodology of first
reducing data dimension by random projection and then quantizing the
projections to ternary or binary codes, which has been widely applied in
classification. Usually, the quantization will seriously degrade the accuracy
of classification due to high quantization errors. Interestingly, however, we
observe that the quantization could provide comparable and often superior
accuracy, as the data to be quantized are sparse features generated with common
filters. Furthermore, this quantization property could be maintained in the
random projections of sparse features, if both the features and random
projection matrices are sufficiently sparse. By conducting extensive
experiments, we validate and analyze this intriguing property.

Computer Vision and Pattern Recognition;Machine Learning