On signal representations within the Bayes decision framework
This work presents new results in the context of minimum probability of error signal representation (MPE-SR) within the Bayes decision framework. These results justify addressing the MPE-SR criterion as a complexity-regularized optimization problem, demonstrating the empirically well understood trade-off between signal representation quality and learning complexity. Contributions are presented in three folds. First, the stipulation of conditions that guarantee a formal tradeoff between approximation and estimation errors under sequence of embedded transformations are provided. Second, the use of this tradeoff to formulate the MPE-SR as a complexity regularized optimization problem, and an approach to address this oracle criterion in practice is given. Finally, formal connections are provided between the MPE-SR criterion and two emblematic feature transformation techniques used in pattern recognition: the optimal quantization problem of classification trees (CART tree pruning algorithms), and some versions of Fisher linear discriminant analysis (LDA).
signal representation bayes decision framework complexity regularization linear discriminant analysis minimum risk decision estimation-approximation error tradeoff mutual information decision trees