Computational Cost Reduction in Learned Transform Classifications

Understand

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.