Dual T: Reducing Estimation Error for Transition Matrix in Label-noise
Learning
- URL: http://arxiv.org/abs/2006.07805v3
- Date: Wed, 23 Jun 2021 05:08:54 GMT
- Title: Dual T: Reducing Estimation Error for Transition Matrix in Label-noise
Learning
- Authors: Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu,
Masashi Sugiyama
- Abstract summary: Existing methods for estimating the transition matrix rely heavily on estimating the noisy class posterior.
We introduce an intermediate class to avoid directly estimating the noisy class posterior.
By this intermediate class, the original transition matrix can then be factorized into the product of two easy-to-estimate transition matrices.
- Score: 157.2709657207203
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The transition matrix, denoting the transition relationship from clean labels
to noisy labels, is essential to build statistically consistent classifiers in
label-noise learning. Existing methods for estimating the transition matrix
rely heavily on estimating the noisy class posterior. However, the estimation
error for noisy class posterior could be large due to the randomness of label
noise, which would lead the transition matrix to be poorly estimated.
Therefore, in this paper, we aim to solve this problem by exploiting the
divide-and-conquer paradigm. Specifically, we introduce an intermediate class
to avoid directly estimating the noisy class posterior. By this intermediate
class, the original transition matrix can then be factorized into the product
of two easy-to-estimate transition matrices. We term the proposed method the
dual-T estimator. Both theoretical analyses and empirical results illustrate
the effectiveness of the dual-T estimator for estimating transition matrices,
leading to better classification performances.
Related papers
Err
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.