弹丸
特征选择
选择(遗传算法)
特征(语言学)
人工智能
计算机科学
无监督学习
一次性
机器学习
模式识别(心理学)
工程类
化学
语言学
哲学
机械工程
有机化学
作者
Atsutoshi Kumagai,Tomoharu Iwata,Yasuhiro Fujiwara
出处
期刊:Cornell University - arXiv
日期:2021-01-01
被引量:1
标识
DOI:10.48550/arxiv.2107.00816
摘要
We propose a few-shot learning method for unsupervised feature selection, which is a task to select a subset of relevant features in unlabeled data. Existing methods usually require many instances for feature selection. However, sufficient instances are often unavailable in practice. The proposed method can select a subset of relevant features in a target task given a few unlabeled target instances by training with unlabeled instances in multiple source tasks. Our model consists of a feature selector and decoder. The feature selector outputs a subset of relevant features taking a few unlabeled instances as input such that the decoder can reconstruct the original features of unseen instances from the selected ones. The feature selector uses the Concrete random variables to select features via gradient descent. To encode task-specific properties from a few unlabeled instances to the model, the Concrete random variables and decoder are modeled using permutation-invariant neural networks that take a few unlabeled instances as input. Our model is trained by minimizing the expected test reconstruction error given a few unlabeled instances that is calculated with datasets in source tasks. We experimentally demonstrate that the proposed method outperforms existing feature selection methods.
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