Quantization: Is It Possible to Improve Classification?

 Large-scale classification poses great challenges to  storage and computation. There are two major solutions to address the problem:  data compression and  quantization. In the paper, we study the method of first reducing data dimension by random projection and then quantizing the projections to ternary and binary codes, which has been widely applied in practice. Often, the extreme quantization would  degrade the accuracy of classification due to high quantization errors. Interestingly, however, we observe that  the quantization could result in   performance improvement, rather than degradation, if the data for quantization are preprocessed by sparse transform.  Also,  the quantization gain could be obtained with the random projections of the data, if both the data and random projection matrices are  sparse enough, such that the resulting projections remain sparse.  The intriguing  performance is verified and analyzed  with extensive experiments.