Feature selection is an important task in effective data mining. A new challenge to feature selection is the so-called "small labeled-sample problem" in which labeled data is small and unlabeled data is large. The paucity of labeled instances provides insufficient information about the structure of the target concept, and can cause supervised feature selection algorithms to fail. Unsupervised feature selection algorithms can work without labeled data. However, these algorithms ignore label information, which may lead to downgraded performance. In this work, we propose to use both (small) labeled and (large) unlabeled data in feature selection, which is a topic has not yet been addressed in feature selection research. We present a semi-supervised feature selection algorithm based on spectral analysis. The algorithm exploits both labeled and unlabeled data through a regularization framework, which provides an effective way to address the "small labeled-sample" problem. Experimental results demonstrated the efficacy of our approach and confirmed that using labeled and unlabeled data together does help feature selection with small labeled samples.
Under the assumption that labeled and unlabeled data are sampled from the same population generated by the target concept, semisupervised feature selection makes use of both labeled and unlabeled data to estimate feature relevance REF .
Semi-supervised Feature Selection via Spectral Analysis
ICDM
Computer Science