ICML 2018oral23 citations
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
Bo Zhao, Xinwei Sun, Yanwei Fu, Yuan Yao, Yizhou Wang
Abstract
It is one typical and general topic of learning a good embedding model to efficiently learn the representation coefficients between two spaces/subspaces. To solve this task, $L_{1}$ regularization is widely used for the pursuit of feature selection and avoiding overfitting, and yet the sparse estimation of features in $L_{1}$ regularization may cause the underfitting of training data. $L_{2}$ regularization is also frequently used, but it is a biased estimator. In this paper, we propose the idea that the features consist of three orthogonal parts,
BibTeX
@InProceedings{pmlr-v80-zhao18c,
title = {{MS}plit {LBI}: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning},
author = {Zhao, Bo and Sun, Xinwei and Fu, Yanwei and Yao, Yuan and Wang, Yizhou},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {5912--5921},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/zhao18c/zhao18c.pdf},
url = {https://proceedings.mlr.press/v80/zhao18c.html},
abstract = {It is one typical and general topic of learning a good embedding model to efficiently learn the representation coefficients between two spaces/subspaces. To solve this task, $L_{1}$ regularization is widely used for the pursuit of feature selection and avoiding overfitting, and yet the sparse estimation of features in $L_{1}$ regularization may cause the underfitting of training data. $L_{2}$ regularization is also frequently used, but it is a biased estimator. In this paper, we propose the idea that the features consist of three orthogonal parts,