Abstract
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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