For two Gaussian classes with equal variance, binary and ternary threshold quantization can increase Fisher feature discrimination, and this can translate into higher classification accuracy than full-precision data in some settings.
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Binary and Ternary Quantization Can Enhance Feature Discrimination
For two Gaussian classes with equal variance, binary and ternary threshold quantization can increase Fisher feature discrimination, and this can translate into higher classification accuracy than full-precision data in some settings.