Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification
Abstract
Theoretically-inspired sequential density ratio estimation (SDRE) algorithms are proposed for the early classification of time series. Conventional SDRE algorithms can fail to estimate DRs precisely due to the internal overnormalization problem, which prevents the DR-based sequential algorithm, Sequential Probability Ratio Test (SPRT), from reaching its asymptotic Bayes optimality. Two novel SPRT-based algorithms, B2Bsqrt-TANDEM and TANDEMformer, are designed to avoid the overnormalization problem for precise unsupervised regression of SDRs. The two algorithms statistically significantly reduce DR estimation errors and classification errors on an artificial sequential Gaussian dataset and real datasets (SiW, UCF101, and HMDB51), respectively. The code is available at: https://github.com/Akinori-F-Ebihara/LLR_saturation_problem.
Cite
@article{arxiv.2302.09810,
title = {Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification},
author = {Akinori F. Ebihara and Taiki Miyagawa and Kazuyuki Sakurai and Hitoshi Imaoka},
journal= {arXiv preprint arXiv:2302.09810},
year = {2023}
}
Comments
Accepted to IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023