  We present a theoretical analysis and empirical evaluations of a novel set of
techniques for computational cost reduction of classifiers that are based on
learned transform and soft-threshold. By modifying optimization procedures for
dictionary and classifier training, as well as the resulting dictionary
entries, our techniques allow to reduce the bit precision and to replace each
floating-point multiplication by a single integer bit shift. We also show how
the optimization algorithms in some dictionary training methods can be modified
to penalize higher-energy dictionaries. We applied our techniques with the
classifier Learning Algorithm for Soft-Thresholding, testing on the datasets
used in its original paper. Our results indicate it is feasible to use solely
sums and bit shifts of integers to classify at test time with a limited
reduction of the classification accuracy. These low power operations are a
valuable trade off in FPGA implementations as they increase the classification
throughput while decrease both energy consumption and manufacturing cost.
